DNA Methylation and Mutational Analysis Methods for Bladder Cancer Surveillance

ABSTRACT

The present disclosure relates to methods of monitoring bladder cancer patients and analyzing patient samples for presence of methylated DNA and optionally particular gene mutations. In some embodiments, analysis results are correlated with clinical outcome measures such as risk of bladder cancer recurrence.

TECHNICAL FIELD

The present disclosure relates to methods of monitoring bladder cancerpatients and analyzing patient samples for presence of methylated DNAand optionally particular gene mutations. In some embodiments, analysisresults are correlated with clinical outcome measures such as risk ofbladder cancer recurrence.

INTRODUCTION

Bladder cancer is one of the most common cancers in industrializedcountries and is one of the costliest cancers to diagnose and monitor.(See R. Siegel et al., CA Cancer J Clin 63: 11-30 (2013); and see, e.g.,T. Reinert Adv. Urol., Article ID 503271, doi:10.1155/2012/503271, pp.1-11 (2012); see also A. Feber et al., Clin. Epigenetics 9:8 doi:10.1186/s13148-016-0303-5 (2017)). For example, the large majority ofnew cases present as non-muscle invasive bladder cancer, with a lowprobability of metastasis, but there remains potential for progressionto a more aggressive disease (e.g., high grade, muscle invasive) andmonitoring patients for development of more aggressive forms of thedisease requires frequent surveillance involving expensive and invasivecystoscopy procedures over a fairly long period of time (e.g., 5 yearsor more). (See, e.g., M. Babjuk et al. Eur. Urol. 59: 997-1008 (2011);B. W. van Rhijn et al., Eur. Urol. 56: 430-42 (2009); M. F. Botteman etal., Pharmacoeconomics 21: 1315-30 (2003).) Cystoscopy, for instance,may be performed in some patients for initial diagnosis and thensubsequently three months after surgical treatment and, if negative,then after intervals of 9 months to 1 year for the next 5 years. Incertain high risk patients, cystoscopy may be performed every 3 monthsfor up to 2 years post-surgery, every 6 months for the subsequent 3years, and then annually thereafter. (See Reinert, page 1.) Thus,noninvasive diagnostic tests may be helpful, for example, as additionsto cystoscopy during this active surveillance treatment period, as theymay provide further information regarding the status of a patient's riskof recurrence. One goal for diagnostic testing in bladder cancer is alsoto allow for cystoscopy tests to be eliminated or reduced in frequencyduring active surveillance.

Previous studies have indicated that DNA methylation, in which a methylgroup is added to the 5^(th) carbon in cytosine in a CpG dinucleotidestretch, may occur inside gene coding sequences, as well as in so-calledCpG islands in the human genome. Generally, CpG dinucleotides in CpGislands are unmethylated in normal cells whereas CpG dinucleotides foundin isolated sequence stretches in gene coding regions may be methylatedin normal cells. The CpG dinucleotides outside of the CpG island regionsmay be significantly less methylated in cancer cells compared to normalcells. (See Reinert at page 4.) Several studies have shown that bladdercancer cells “leak” DNA into urine and that this DNA can be detected bylooking at methylation of genes in urine samples. (Id. at pages 4-5,Table 3.) The present disclosure describes gene sets and algorithms forassessing risk of recurrence in bladder cancer patients from examinationof DNA methylation as well as presence of gene mutations from urinesediment samples.

SUMMARY

This application discloses methods of monitoring patients with bladdercancer, comprising, for example, obtaining a urine sediment sample froma patient; bisulfite converting DNA from the sample; performingmethylation-specific quantitative PCR on the bisulfite converted DNA todetermine the amount and/or the concentration of methylated DNA in thesample from a set of genes comprising at least four genes selected fromMEIS1, NKPD1, ONECUT2, KLF2, OSR1, SOX1, EOMES, DDX25, and TMEM106A andfrom at least one reference gene. In some embodiments, the patient hasbeen diagnosed with non-muscle-invasive bladder cancer. In someembodiments, the patient's tumor has previously been diagnosed as Ta,Tis, T0, or low grade. In some embodiments, the patient's tumor haspreviously been diagnosed as T1 or high grade. In some embodiments, thepatient has been diagnosed with muscle-invasive bladder cancer. In someembodiments, the method is conducted prior to cystoscopy. In someembodiments, the method is conducted after a negative cystoscopy. Insome embodiments, the method is conducted at least once per year, suchas once per six months, or once per three months, for example, as partof an active surveillance visit.

In some embodiments, at least four genes comprise MEIS1, NKPD1, ONECUT2,and KLF2. In some embodiments, the reference genes comprise one or moreof CTNS, TOP3A, COL2A and SLC24A3. In some embodiments, the set of genesfurther comprises at least one gene selected from EPHX3, IRX5, NID2,VIM, and ITPKB. In some embodiments, the set of genes comprises one ofthe following gene sets along with at least one reference gene: (a) M1:MEIS1, NKPD1, ONECUT2, KLF2; (b) M2: MEIS1, NKPD1, ONECUT2, OSR1; (c)M3: MEIS1, NKPD1, KLF2, SOX1; (d) M4: ONECUT2, OSR1, SOX1, EOMES; (e)M5: MEIS1, NKPD1, ONECUT2, OSR1, TMEM106A; EPHX3; (f) M6: MEIS1, NKPD1,ONECUT2, KLF2, TMEM106A, IRX5; (g) M7: MEIS1, NKPD1, ONECUT2, KLF2,SOX1, TMEM106A; (h) M8: ONECUT2, OSR1, SOX1, EOMES, NID2, VIM; and (i)M9: MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, ITPKB. In some embodiments,the set of genes consists of one of the following gene sets: (a) M1:MEIS1, NKPD1, ONECUT2, KLF2, and at least one reference gene; (b) M2:MEIS1, NKPD1, ONECUT2, OSR1, and at least one reference gene; (c) M3:MEIS1, NKPD1, KLF2, SOX1, and at least one reference gene; (d) M4:ONECUT2, OSR1, SOX1, EOMES, and at least one reference gene; (e) M5:MEIS1, NKPD1, ONECUT2, OSR1, TMEM106A; EPHX3, and at least one referencegene; (f) M6: MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, IRX5, and at leastone reference gene; (g) M7: MEIS1, NKPD1, ONECUT2, KLF2, SOX1, TMEM106A,and at least one reference gene; (h) M8: ONECUT2, OSR1, SOX1, EOMES,NID2, VIM, and at least one reference gene; and (i) M9: MEIS1, NKPD1,ONECUT2, KLF2, TMEM106A, ITPKB, and at least one reference gene.

In some embodiments, the methods further comprise determining a risk ofrecurrence for the patient or determining presence of an actualrecurrence, comprising comparing the amount and/or concentration ofmethylation for the patient to the amount and/or concentration ofmethylation for a reference set of bladder cancer patients. In someembodiments, the patient has previously been diagnosed to be at low riskof recurrence and the method is conducted, for example, to confirm thisdiagnosis and/or to confirm the absence of any actual recurrence. Insome embodiments, a patient has previously been determined to have a lowrisk of recurrence based on a Ta or low grade tumor sample comprisingtissue from TURBT, cystoscopy, or from an upper GU workup. In someembodiments, the low risk patient has either normal or abnormalcystoscopy. In some embodiments, the low risk patient has either normalor abnormal cytology. In some embodiments, the patient has previouslybeen diagnosed to be at high risk of recurrence and the method isconducted, for example, to confirm this diagnosis and to confirm theabsence of any actual recurrence. For example, in some embodiments, apatient has previously been determined to have a high risk of recurrencebased on a high grade, T1-T2, or Tis tumor sample comprising tissue fromTURBT, cystoscopy, or from an upper GU workup. In some embodiments, thehigh risk patient has either normal or abnormal cystoscopy. In someembodiments, the high risk patient has either normal or abnormalcytology.

In some embodiments, the method further comprises analyzing thebisulfate converted DNA for mutations in one or both of FGFR3 and TERT.In some embodiments, the FGFR3 mutation is at chromosome positionch4:1803568 and the TERT mutation is at chromosome positionchr5:1295228. In some embodiments, the method further comprisescomparing the presence or absence of mutations in FGFR3 and/or TERT tothe prevalence of the FGFR3 and TERT mutations in the reference set ofbladder cancer patients.

The instant disclosure also concerns methods of treating a bladdercancer patient having a low risk of recurrence, comprising, for example,obtaining a urine sediment sample from the patient; bisulfite convertingDNA from the sample; performing methylation-specific quantitative PCR onthe bisulfite converted DNA to determine the amount and/or theconcentration of methylated DNA in the sample from a set of genescomprising at least four genes selected from MEIS1, NKPD1, ONECUT2,KLF2, OSR1, SOX1, EOMES, DDX25, and TMEM106A and from at least onereference gene; determining that the patient has a low risk ofrecurrence by comparing the amount and/or concentration of methylationfor the patient to the amount and/or concentration of methylation for areference set of bladder cancer patients; and treating the patient withactive surveillance. In some embodiments, the treatment with activesurveillance comprises reducing the frequency of cystoscopy for thepatient, for example, when the low risk status of the patient isconfirmed by the method. In some embodiments, the patient has beendiagnosed with non-muscle-invasive bladder cancer. In some embodiments,the patient's tumor has previously been diagnosed as Ta, Tis, T0, or lowgrade. In some embodiments, the patient's tumor has previously beendiagnosed as T1 or high grade. In some embodiments, the method isconducted prior to cystoscopy. In some embodiments, the method isconducted after a negative cystoscopy.

In some embodiments of the above treatment methods, at least four genescomprise MEIS1, NKPD1, ONECUT2, and KLF2. In some embodiments, thereference genes comprise one or more of CTNS, TOP3A, COL2A and SLC24A3.In some embodiments, the set of genes further comprises at least onegene selected from EPHX3, IRX5, NID2, VIM, and ITPKB. In someembodiments, the set of genes comprises one of the following gene setsalong with at least one reference gene: (a) M1: MEIS1, NKPD1, ONECUT2,KLF2; (b) M2: MEIS1, NKPD1, ONECUT2, OSR1; (c) M3: MEIS1, NKPD1, KLF2,SOX1; (d) M4: ONECUT2, OSR1, SOX1, EOMES; (e) M5: MEIS1, NKPD1, ONECUT2,OSR1, TMEM106A; EPHX3; (0 M6: MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A,IRX5; (g) M7: MEIS1, NKPD1, ONECUT2, KLF2, SOX1, TMEM106A; (h) M8:ONECUT2, OSR1, SOX1, EOMES, NID2, VIM; and (i) M9: MEIS1, NKPD1,ONECUT2, KLF2, TMEM106A, ITPKB. In some embodiments, the set of genesconsists of one of the following gene sets: (a) M1: MEIS1, NKPD1,ONECUT2, KLF2, and at least one reference gene; (b) M2: MEIS1, NKPD1,ONECUT2, OSR1, and at least one reference gene; (c) M3: MEIS1, NKPD1,KLF2, SOX1, and at least one reference gene; (d) M4: ONECUT2, OSR1,SOX1, EOMES, and at least one reference gene; (e) M5: MEIS1, NKPD1,ONECUT2, OSR1, TMEM106A; EPHX3, and at least one reference gene; (f) M6:MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, IRX5, and at least one referencegene; (g) M7: MEIS1, NKPD1, ONECUT2, KLF2, SOX1, TMEM106A, and at leastone reference gene; (h) M8: ONECUT2, OSR1, SOX1, EOMES, NID2, VIM, andat least one reference gene; and (i) M9: MEIS1, NKPD1, ONECUT2, KLF2,TMEM106A, ITPKB, and at least one reference gene. In some embodiments,the method further comprises analyzing the bisulfate converted DNA formutations in one or both of FGFR3 and TERT. In some embodiments, theFGFR3 mutation is at chromosome position ch4:1803568 and the TERTmutation is at chromosome position chr5:1295228. In some embodiments,the method further comprises comparing the presence or absence ofmutations in FGFR3 and/or TERT to that of the reference set of bladdercancer patients.

The instant disclosure also relates to systems for quantifying DNAmethylation in a urine sediment sample from a bladder cancer patient. Insome embodiments, the systems may comprise (a) at least one cartridgewith at least one well, the at least one well comprising primers foramplification of bisulfate modified DNA of at least four genes selectedfrom MEIS1, NKPD1, ONECUT2, KLF2, OSR1, SOX1, EOMES, DDX25, andTMEM106A, and at least one reference gene, wherein the cartridge isconfigured for performance of methylation-specific quantitative PCR onthe genes, and wherein the primers are optionally attached to the atleast one well; (b) a detection device for detecting amplified DNA fromeach gene; and (c) computer software for quantifying the amount and/orconcentration of methylated DNA from the genes, wherein the softwareoptionally compares the amount and/or concentration of methylation forthe patient to the amount and/or concentration of methylation for areference set of bladder cancer patients. In some embodiments, thecartridge is configured for performance of methylation-specificquantitative PCR on a set of genes comprising MEIS1, NKPD1, ONECUT2, andKLF2, and at least one reference gene. In some embodiments, thecartridge is configured for performance of methylation-specificquantitative PCR on a set of genes comprising MEIS1, NKPD1, ONECUT2, andKLF2, and one or more of OSR1, SOX1, EOMES, DDX25, and TMEM106A, and atleast one reference gene. In some embodiments, the cartridge isconfigured for performance of methylation-specific quantitative PCR on aset of genes comprising MEIS1, NKPD1, ONECUT2, and KLF2, and one or moreof EPHX3, IRX5, NID2, VIM, and ITPKB, and at least one reference gene.In some embodiments, the cartridge is configured for performance ofmethylation-specific quantitative PCR on a set of genes comprising oneof the following gene sets: (a) M1: MEIS1, NKPD1, ONECUT2, KLF2, and atleast one reference gene; (b) M2: MEIS1, NKPD1, ONECUT2, OSR1, and atleast one reference gene; (c) M3: MEIS1, NKPD1, KLF2, SOX1, and at leastone reference gene; (d) M4: ONECUT2, OSR1, SOX1, EOMES, and at least onereference gene; (e) M5: MEIS1, NKPD1, ONECUT2, OSR1, TMEM106A; EPHX3,and at least one reference gene; (0 M6: MEIS1, NKPD1, ONECUT2, KLF2,TMEM106A, IRX5, and at least one reference gene; (g) M7: MEIS1, NKPD1,ONECUT2, KLF2, SOX1, TMEM106A, and at least one reference gene; (h) M8:ONECUT2, OSR1, SOX1, EOMES, NID2, VIM, and at least one reference gene;and (i) M9: MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, ITPKB, and at leastone reference gene. In some embodiments, the cartridge is configured forperformance of methylation-specific quantitative PCR on a set of genesconsisting of one of the following gene sets: (a) M1: MEIS1, NKPD1,ONECUT2, KLF2, and at least one reference gene; (b) M2: MEIS1, NKPD1,ONECUT2, OSR1, and at least one reference gene; (c) M3: MEIS1, NKPD1,KLF2, SOX1, and at least one reference gene; (d) M4: ONECUT2, OSR1,SOX1, EOMES, and at least one reference gene; (e) M5: MEIS1, NKPD1,ONECUT2, OSR1, TMEM106A; EPHX3, and at least one reference gene; (0 M6:MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, IRX5, and at least one referencegene; (g) M7: MEIS1, NKPD1, ONECUT2, KLF2, SOX1, TMEM106A, and at leastone reference gene; (h) M8: ONECUT2, OSR1, SOX1, EOMES, NID2, VIM, andat least one reference gene; and (i) M9: MEIS1, NKPD1, ONECUT2, KLF2,TMEM106A, ITPKB, and at least one reference gene. In some embodiments,the at least one reference gene comprises one or more of CTNS, TOP3A,COL2A and SLC24A3. In some embodiments, the primers are attached to theat least one well of the cartridge. In some embodiments, the cartridgefurther comprises reagents for detecting mutations in one or both ofTERT and FGFR3. In some embodiments, the FGFR3 mutation is FGFR3 is a Cto G substitution at chr4:1803568 and wherein the TERT mutation is a Gto A substitution at chr5:1295228. In some embodiments, the system iscapable of performing the above methods of methylation quantitation in abladder cancer patient urine sediment sample.

The present disclosure also relates to cartridges comprising at leastone well comprising primers for amplification of bisulfate modified DNAof at least four genes selected from MEIS1, NKPD1, ONECUT2, KLF2, OSR1,SOX1, EOMES, DDX25, and TMEM106A, and at least one reference gene,wherein the cartridge is configured for performance ofmethylation-specific quantitative PCR on the genes, and wherein theprimers are optionally attached to the at least one well. The cartridgesmay be components of the above systems, or they may be independent ofthe above systems. For example, cartridges may be configured to workwith a variety of detection apparatuses and systems. In someembodiments, the cartridge is configured for performance ofmethylation-specific quantitative PCR on a set of genes comprisingMEIS1, NKPD1, ONECUT2, and KLF2, and at least one reference gene. Insome embodiments, the set of genes comprises MEIS1, NKPD1, ONECUT2, andKLF2, and one or more of OSR1, SOX1, EOMES, DDX25, and TMEM106A, and atleast one reference gene. In some embodiments, the set of genescomprises MEIS1, NKPD1, ONECUT2, and KLF2, and one or more of EPHX3,IRX5, NID2, VIM, and ITPKB, and at least one reference gene. In someembodiments, the cartridge is configured for performance ofmethylation-specific quantitative PCR on a set of genes comprising oneof the following gene sets: (a) M1: MEIS1, NKPD1, ONECUT2, KLF2, and atleast one reference gene; (b) M2: MEIS1, NKPD1, ONECUT2, OSR1, and atleast one reference gene; (c) M3: MEIS1, NKPD1, KLF2, SOX1, and at leastone reference gene; (d) M4: ONECUT2, OSR1, SOX1, EOMES, and at least onereference gene; (e) M5: MEIS1, NKPD1, ONECUT2, OSR1, TMEM106A; EPHX3,and at least one reference gene; (0 M6: MEIS1, NKPD1, ONECUT2, KLF2,TMEM106A, IRX5, and at least one reference gene; (g) M7: MEIS1, NKPD1,ONECUT2, KLF2, SOX1, TMEM106A, and at least one reference gene; (h) M8:ONECUT2, OSR1, SOX1, EOMES, NID2, VIM, and at least one reference gene;and (i) M9: MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, ITPKB, and at leastone reference gene. In some embodiments, the cartridge is configured forperformance of methylation-specific quantitative PCR on a set of genesconsisting of one of the following gene sets: (a) M1: MEIS1, NKPD1,ONECUT2, KLF2, and at least one reference gene; (b) M2: MEIS1, NKPD1,ONECUT2, OSR1, and at least one reference gene; (c) M3: MEIS1, NKPD1,KLF2, SOX1, and at least one reference gene; (d) M4: ONECUT2, OSR1,SOX1, EOMES, and at least one reference gene; (e) M5: MEIS1, NKPD1,ONECUT2, OSR1, TMEM106A; EPHX3, and at least one reference gene; (0 M6:MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, IRX5, and at least one referencegene; (g) M7: MEIS1, NKPD1, ONECUT2, KLF2, SOX1, TMEM106A, and at leastone reference gene; (h) M8: ONECUT2, OSR1, SOX1, EOMES, NID2, VIM, andat least one reference gene; and (i) M9: MEIS1, NKPD1, ONECUT2, KLF2,TMEM106A, ITPKB, and at least one reference gene. In some embodiments,the at least one reference gene comprises one or more of CTNS, TOP3A,COL2A and SLC24A3. In some embodiments, the primers are attached to theat least one well of the cartridge. In some embodiments, the cartridgefurther comprises reagents for detecting mutations in one or both ofTERT and FGFR3. In some embodiments, the FGFR3 mutation is FGFR3 is a Cto G substitution at chr4:1803568 and wherein the TERT mutation is a Gto A substitution at chr5:1295228. The above cartridges may be used inthe methods and systems further above.

The disclosure also contemplates kits comprising the cartridgesdescribed above. Kits may further comprise at least one of thefollowing: (a) deoxyribonucleotide triphosphates dTTP, dATP, dCTP anddGTP; (b) at least one DNA polymerase enzyme; and (c) at least onereaction and/or wash buffer. The kits may, for example, be components ofthe systems described above and may be useful in performing the methodsdescribed earlier in this section. In some embodiments, the kitscomprise each of (a) deoxyribonucleotide triphosphates dTTP, dATP, dCTPand dGTP; (b) at least one DNA polymerase enzyme; and (c) at least onereaction and/or wash buffer. In some embodiments, the kits furthercomprise at least one detection reagent. In some embodiments, the kitsfurther comprise at least one reagent for bisulfite conversion of DNA.In some embodiments, the kits further comprise reagents for reagents fordetecting mutations in one or both of TERT and FGFR3. In some suchembodiments, the FGFR3 mutation is FGFR3 is a C to G substitution atchr4:1803568 and wherein the TERT mutation is a G to A substitution atchr5:1295228. The kits herein may further comprise instructions for use.

Also contemplated herein are compositions comprising a set of primersfor PCR amplification of bisulfite modified DNA of at least four genesselected from MEIS1, NKPD1, ONECUT2, KLF2, OSR1, SOX1, EOMES, DDX25, andTMEM106A, and at least one reference gene. In some compositions, the setof primers is for a group of genes consisting of at least four genesselected from MEIS1, NKPD1, ONECUT2, KLF2, OSR1, SOX1, EOMES, DDX25, andTMEM106A, and at least one reference gene. In some compositions herein,the genes comprise MEIS1, NKPD1, ONECUT2, and KLF2, and at least onereference gene. In some compositions herein, the genes consist of MEIS1,NKPD1, ONECUT2, and KLF2, and at least one reference gene. In somecompositions herein, the genes comprise MEIS1, NKPD1, ONECUT2, and KLF2,and one or more of OSR1, SOX1, EOMES, DDX25, and TMEM106A, and at leastone reference gene. In some compositions herein, the genes consist ofMEIS1, NKPD1, ONECUT2, and KLF2, and one or more of OSR1, SOX1, EOMES,DDX25, and TMEM106A, and at least one reference gene. In somecompositions herein, the genes comprise MEIS1, NKPD1, ONECUT2, and KLF2,and one or more of EPHX3, IRX5, NID2, VIM, and ITPKB, and at least onereference gene. In some compositions herein, the genes consist of MEIS1,NKPD1, ONECUT2, and KLF2, and one or more of EPHX3, IRX5, NID2, VIM, andITPKB, and at least one reference gene. Some compositions hereincomprise a set of primers for methylation-specific quantitative PCR of aset of genes comprising: (a) MEIS1, NKPD1, ONECUT2, KLF2, and at leastone reference gene; (b) MEIS1, NKPD1, ONECUT2, OSR1, and at least onereference gene; (c) MEIS1, NKPD1, KLF2, SOX1, and at least one referencegene; (d) ONECUT2, OSR1, SOX1, EOMES, and at least one reference gene;(e) MEIS1, NKPD1, ONECUT2, OSR1, TMEM106A; EPHX3, and at least onereference gene; (0 MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, IRX5, and atleast one reference gene; (g) MEIS1, NKPD1, ONECUT2, KLF2, SOX1,TMEM106A, and at least one reference gene; (h) ONECUT2, OSR1, SOX1,EOMES, NID2, VIM, and at least one reference gene; or (i) MEIS1, NKPD1,ONECUT2, KLF2, TMEM106A, ITPKB, and at least one reference gene. Somecompositions herein comprise a set of primers for methylation-specificquantitative PCR of a set of genes consisting of: (a) MEIS1, NKPD1,ONECUT2, KLF2, and at least one reference gene; (b) MEIS1, NKPD1,ONECUT2, OSR1, and at least one reference gene; (c) MEIS1, NKPD1, KLF2,SOX1, and at least one reference gene; (d) ONECUT2, OSR1, SOX1, EOMES,and at least one reference gene; (e) MEIS1, NKPD1, ONECUT2, OSR1,TMEM106A; EPHX3, and at least one reference gene; (0 MEIS1, NKPD1,ONECUT2, KLF2, TMEM106A, IRX5, and at least one reference gene; (g)MEIS1, NKPD1, ONECUT2, KLF2, SOX1, TMEM106A, and at least one referencegene; (h) ONECUT2, OSR1, SOX1, EOMES, NID2, VIM, and at least onereference gene; or (i) MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, ITPKB, andat least one reference gene. In some compositions herein, the at leastone reference gene comprises one or more of CTNS, TOP3A, COL2A andSLC24A3. Some compositions herein further comprise reagents fordetecting mutations in one or both of TERT and FGFR3. In someembodiments, the FGFR3 mutation is FGFR3 is a C to G substitution atchr4:1803568 and wherein the TERT mutation is a G to A substitution atchr5:1295228. Some compositions herein further comprising at least oneof the following: (a) deoxyribonucleotide triphosphates dTTP, dATP, dCTPand dGTP; (b) at least one DNA polymerase enzyme; (c) at least onereaction and/or wash buffer. In some embodiments, the compositioncomprises each of (a) deoxyribonucleotide triphosphates dTTP, dATP, dCTPand dGTP; (b) at least one DNA polymerase enzyme; and (c) at least onereaction and/or wash buffer. In some embodiments, the compositionfurther comprises at least one detection reagent. In some embodiments,the composition further comprises at least one reagent for bisulfiteconversion of DNA. Compositions herein may further be components of theabove-described systems, cartridges, and kits, and may be useful in themethods described earlier in this section.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 provides a performance summary for analysis of the amount ofmethylated DNA for patients who have initially been diagnosed as havinga low risk of recurrence (“initial low risk” patients) according to theM1 plus mutation algorithm, as described in Example 3 below.Specifically, FIG. 1 provides negative predictive value (NPV), positivepredictive value (PPV), sensitivity, and specificity results forpredicting patients in that initial low risk pool who actually are athigh risk of recurrence.

FIG. 2 provides further data of a performance summary for the M1 plusmutation analysis of the amount of methylated DNA for initial low riskof recurrence patients, as described in Example 3 below. Specifically,FIG. 2 provides negative predictive value (NPV), positive predictivevalue (PPV), sensitivity, and specificity data for predicting those lowrisk patients who actually have signs of recurrence.

FIG. 3 provides additional data of a performance summary for the M1 plusmutation analysis of the amount of methylated DNA for initial low riskof recurrence patients, as described in Example 3 below. Specifically,FIG. 3 provides predictiveness curves and ROC curves based on the datain FIGS. 1 and 2.

FIG. 4 provides a performance summary for the M1 plus mutation analysisof the amount of methylated DNA for initial low risk of recurrencepatients, as described in Example 3 below. Specifically, FIG. 4 providesnegative predictive value (NPV), positive predictive value (PPV),sensitivity, and specificity data for predicting those high riskpatients who are actually at high risk of recurrence.

FIG. 5 provides further data of a performance summary for the M1 plusmutation analysis of the amount of methylated DNA for initial low riskof recurrence patients, as described in Example 3 below. Specifically,FIG. 5 provides negative predictive value (NPV), positive predictivevalue (PPV), sensitivity, and specificity data for predicting those highrisk patients who actually have signs of recurrence.

FIG. 6 provides additional data of a performance summary for the M1 plusmutation analysis of the amount of methylated DNA for initial low riskof recurrence patients, as described in Example 3 below. Specifically,FIG. 6 provides predictiveness curves and ROC curves based on the datain FIGS. 4 and 5.

FIG. 7 provides a performance summary for the M1 plus mutation analysisof the concentration of methylated DNA for initial low risk ofrecurrence patients, as described in Example 3 below. Specifically, FIG.1 provides negative predictive value (NPV), positive predictive value(PPV), sensitivity, and specificity data for predicting those low riskpatients who are actually at high risk of recurrence.

FIG. 8 provides further data of a performance summary for the M1 plusmutation analysis of the concentration of methylated DNA for initial lowrisk of recurrence patients, as described in Example 3 below.Specifically, FIG. 8 provides negative predictive value (NPV), positivepredictive value (PPV), sensitivity, and specificity data for predictingthose low risk patients who actually have signs of recurrence.

FIG. 9 provides additional data of a performance summary for the M1 plusmutation analysis of the concentration of methylated DNA for initial lowrisk of recurrence patients, as described in Example 3 below.Specifically, FIG. 9 provides predictiveness curves and ROC curves basedon the data in FIGS. 7 and 8.

FIG. 10 provides a performance summary for the M1 plus mutation analysisof the concentration of methylated DNA for initial high risk ofrecurrence patients, as described in Example 3 below. Specifically, FIG.10 provides negative predictive value (NPV), positive predictive value(PPV), sensitivity, and specificity data for predicting those high riskpatients who are actually at high risk of recurrence.

FIG. 11 provides further data of a performance summary for the M1 plusmutation analysis of the concentration of methylated DNA for initialhigh risk of recurrence patients, as described in Example 3 below.Specifically, FIG. 11 provides negative predictive value (NPV), positivepredictive value (PPV), sensitivity, and specificity data for predictingthose high-risk patients who actually have signs of recurrence.

FIG. 12 provides additional data of a performance summary for the M1plus mutation analysis of the concentration of methylated DNA forinitial high risk of recurrence patients, as described in Example 3below. Specifically, FIG. 12 provides predictiveness curves and ROCcurves based on the data in FIGS. 10 and 11.

FIG. 13 shows a flow chart of steps in a methylation assay process asdisclosed herein.

DETAILED DESCRIPTION Definitions

Unless defined otherwise, technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Singleton et al., Dictionary ofMicrobiology and Molecular Biology 2nd ed., J. Wiley & Sons (New York,N.Y. 1994), and March, Advanced Organic Chemistry Reactions, Mechanismsand Structure 4th ed., John Wiley & Sons (New York, N.Y. 1992), provideone skilled in the art with a general guide to many of the terms used inthe present application.

One skilled in the art will recognize many methods and materials similaror equivalent to those described herein, which could be used in thepractice of the present invention. Indeed, the present invention is inno way limited to the methods and materials described herein. Forpurposes of the invention, the following terms are defined below.

The terms “tumor” and “lesion” as used herein, refer to all neoplasticcell growth and proliferation, whether malignant or benign, and allpre-cancerous and cancerous cells and tissues. Those skilled in the artwill realize that a tumor tissue sample may comprise multiple biologicalelements, such as one or more cancer cells, partial or fragmented cells,tumors in various stages, surrounding histologically normal-appearingtissue, and/or macro or micro-dissected tissue.

The terms “cancer,” “cancerous,” and “carcinoma” refer to or describethe physiological condition in mammals that is typically characterizedby unregulated cell growth. As used herein, the term “bladder cancer”refers to cancer that has arisen from the bladder. Examples of bladdercancer include, for example, “non-muscle invasive bladder cancer” or“NMIBC” and “muscle invasive bladder cancer” or “MIBC.”

The America Joint Committee on Cancer (AJCC) TNM staging system (7^(th)ed., 2010) may be used to stage bladder cancer. The TNM staging systemconsiders (T) how far the main or primary tumor has grown through thebladder wall and whether it has grown into nearby tissues, (N) whetherthe cancer has spread to lymph nodes near the bladder, and (M) whetheror not the cancer has metastasized to distant sites such as other organsor lymph nodes not near the bladder. The TNM staging system for bladdercancer is as follows:

Primary Tumor (T) Tx Primary tumor cannot be assessed T0 No evidence ofprimary tumor Ta Non-invasive papillary carcinoma Tis Non-invasive flatcarcinoma (flat carcinoma in situ, or CIS) T1 Tumor has grown from thelayer of cells lining the bladder into the connective tissue below, buthas not grown into the muscle layer of the bladder T2 Tumor has growninto the muscle layer T3 Tumor has growth through the muscle layer intothe fatty tissue surrounding it T4 Tumor has spread beyond the fattytissue into nearby organs or structures including any of: prostatestroma, seminal vesicles, uterus, vagina, pelvic wall, or abdominal wallRegional Lymph Nodes (N) NX Regional lymph nodes cannot be assessed N0No regional lymph node metastasis N1 Cancer has spread to a singleregional lymph node in the true pelvis N2 Cancer has spread to two ormore lymph nodes in the true pelvis N3 Cancer has spread to lymph nodesalong the common iliac artery Distant Metastasis (M) M0 No distantmetastasis M1 Distant metastasis has occurred Bladder Cancer StagesStage 0a Ta N0 M0 Stage 0is Tis N0 M0 Stage 1 T1 N0 M0 Stage II T2 N0 M0Stage III T3 or N0 M0 (if T4, the cancer may have spread T4 to prostate,uterus, or vagina but not through the abdominal wall) Stage IV T4 Any NM0 (in which the T4 cancer has spread through the abdominal wall) Any TN1 to N3 M0 Any T Any N M1

Reference to tumor “stage,” as used herein, refers to how far thetumor(s) has spread, and refers to any of the Stages I, II, III, or IVabove for bladder cancer, which are based on the T, N, and M criteriagiven above.

Reference to tumor “grade,” as used herein, refers to the grading ofbladder cancer, based on how the cancer cells look under a microscope.“Low grade” (also called “well-differentiated”) cancers look more likenormal tissue under a microscope. “High grade” (also called “poorlydifferentiated” or “undifferentiated”) cancers look less like normaltissue under a microscope. Low grade cancers tend to have a betterprognosis than high grade cancers.

The term “prognosis” is used herein to refer to the prediction of thelikelihood that a cancer patient will have a cancer-attributable deathor progression, including recurrence, metastatic spread, and drugresistance, of a neoplastic disease, such as bladder cancer.

The term “prediction” or “predict” is used herein to refer to thelikelihood that a cancer patient will have a particular response totreatment, whether positive (aka. a “beneficial response”) or negative,following surgical removal of the primary tumor. For example, treatmentcould include targeted drugs, immunotherapy, or chemotherapy.

Unless indicated otherwise, each gene name used herein corresponds tothe Official Symbol assigned to the gene and provided by Entrez Gene(URL: www (dot) ncbi (dot) nlm (dot) nih (dot) gov (slash) sites (slash)entrez) as of the filing date of this application.

The terms “correlated” and “associated” are used interchangeably hereinto refer to the association between two different measurements orbetween a measurement or series of measurements, such as an amount orconcentration of methylation in a sample or presence of a mutation, andan event, such as recurrence.

The terms “recurrence” and “relapse” are used herein, in the context ofpotential clinical outcomes of bladder cancer, refer to recurrence ofeither local or distant metastases. Identification of a recurrence couldbe done by, for example, one or more of cystoscopy, cytology, CTimaging, ultrasound, arteriogram, or X-ray, biopsy, urine or blood test,physical exam, or research center tumor registry.

As used herein, “cystoscopy” refers to a procedure in which a cystoscopeinstrument is inserted into the urethra to allow visualization of theurethra and urinary bladder, and optionally, to remove tissue samplesfor further analysis.

As used herein, a “transurethral resection” or “transurethral resectionof bladder tumor” (TUR or TURBT) refers to a procedure in which a doctorremoves suspected tumor tissue and optionally surrounding tumor muscletissue, for example, to determine if the tumor is indeed cancerous andif it has invaded surrounding muscle tissue. TURBT and subsequentanalysis can be used, for example, to determine stage and grade of atumor.

As used herein, “cytology” refers to a process of examining bladdertissue or washings from cystology or TURBT, or of examining a urinesample under a microscope to look for the presence of potential bladdercancer cells and/or to assess the morphology of the cells to determinethe grade of the cells.

The terms “surgery” or “surgical resection” are used herein to refer tosurgical removal of some or all of a tumor, and usually some of thesurrounding tissue. Examples of surgical techniques include TURBT,laparoscopic procedures, biopsy, or tumor ablation, such as cryotherapy,radio frequency ablation, and high intensity ultrasound. In cancerpatients, the extent of tissue removed during surgery depends on thestate of the tumor as observed by a surgeon.

The term “active surveillance,” when applied to a bladder cancerpatient, refers to the process of monitoring such a patient afterprimary treatment for bladder cancer, for example, to check forrecurrence events, or to determine the risk of a future recurrence inthe patient. A “surveillance visit,” for example of a patient to anoncologist or other doctor, refers to a visit intended as part of thepatient's surveillance procedure, such as a visit in which a urine test,cystoscopy procedure, or the like is performed. A patient under activesurveillance may have, for example, 1, 2, 3, or 4 surveillance visitsper year depending on the patient's recurrence risk and the stage orgrade of the cancer. Cystoscopy may be performed at one or more of suchannual visits.

The term “Cp” as used herein refers to “crossing point.” The Cp value iscalculated by determining the second derivatives of entire qPCRamplification curves and their maximum value. The Cp value representsthe cycle at which the increase of fluorescence is highest and where thelogarithmic phase of a PCR amplification process begins.

The term “methylation fraction” or “MF” refers to an estimation of thepercentage or fraction of a particular gene or set of genes that hasbeen methylated. Methylation may be estimated from amethylation-specific Cp according to mathematical formulas describedherein.

The term “amount of methylated DNA” in a sample refers to the MFmultiplied by the sample DNA yield, and may be measured in gram-basedunits such as nanograms. The amount of methylated DNA may be determinedfor each gene and then averaged to obtain a “mean amount of methylatedDNA” for the sample. The term “concentration of methylated DNA” in asample refers to the MF multiplied by the sample DNA yield over thesample volume. The concentration of methylated DNA may be determined innanograms per mL units, for example, and it may be determined for eachgene and then averaged to obtain a “mean concentration of methylatedDNA” for the sample.

A “reference set of bladder cancer patients,” for example, may be usedto estimate how a particular patient's methylation and optionallymutation data compares to data from bladder cancer patients as a whole.Thus, a reference set may include patients known to have both low andhigh risks of recurrence or who were known to have had a recurrence ornot to have recurred. Data from a reference set may therefore provide arange of possible recurrence risks correlated with amount orconcentration of methylated DNA, for example, against which new data foran individual patient may be compared.

The term “Hazard Ratio (HR)” as used herein refers to the effect of anexplanatory variable on the hazard or risk of an event (i.e. recurrenceor death). In proportional hazards regression models, the HR is theratio of the predicted hazard for two groups (e.g. patients with twodifferent stages of cancer) or for a unit change in a continuousvariable (e.g. one standard deviation change).

The term “negative predictive value” or “NPV” is the probability that asubject with a negative result in a diagnostic assay actually does nothave the condition or risk for which the subject is being tested. It maybe expressed as the number of true negative subjects divided by the sumof the true negative and the false negative, expressed as a percentage.

The term “positive predictive value” or “PPV” is the probability that asubject with a positive result in a diagnostic assay actually does havethe condition or risk for which the subject is being tested. It may beexpressed as the number of true positive subjects divided by the sum ofthe true positive and the false positive, expressed as a percentage.

The term “sensitivity” refers to the ability of a diagnostic test tocorrectly identify those having the condition that the test is intendedto diagnose (i.e. the true positive rate). Thus, if a test is highlysensitive, for example, a patient with a negative result can be moreconfident that the negative result is accurate.

The term “specificity” refers to the ability of a diagnostic test tocorrectly rule out subjects who do not have the condition being tested(i.e. the true negative rate). Thus, a highly specific test may have alow rate of false positive results.

The term “polynucleotide,” when used in singular or plural generallyrefers to any polyribonucleotide or polydeoxyribonucleotide, which maybe unmodified RNA or DNA or modified RNA or DNA. Thus, for instance,polynucleotides are defined herein to include, without limitation,single- and double-stranded RNA, and RNA including single- anddouble-stranded regions, hybrid molecules comprising DNA and RNA thatmay be single-stranded or, more typically, double-stranded or includesingle- and double-stranded regions. In addition, the term“polynucleotide” as used herein refers to triple-stranded regionscomprising RNA or DNA or both RNA and DNA. The strands in such regionsmay be from the same molecule or from different molecules. The regionsmay include all of one or more of the molecules, but more typicallyinvolve only a region of some of the molecules. One of the molecules ofa triple-helical region often is an oligonucleotide. The term“polynucleotide” specifically includes cDNAs. The term includes DNAs(including cDNAs) and RNAs that contain one or more modified bases.Thus, DNAs or RNAs with backbones modified for stability or for otherreasons, are “polynucleotides” as that term is intended herein.Moreover, DNAs or RNAs comprising unusual bases, such as inosine, ormodified bases, such as tritiated bases, are included within the term“polynucleotides” as defined herein. In general, the term“polynucleotide” embraces all chemically, enzymatically and/ormetabolically modified forms of unmodified polynucleotides, as well asthe chemical forms of DNA and RNA characteristic of viruses and cells,including simple and complex cells.

The term “oligonucleotide” refers to a relatively short polynucleotide,including, without limitation, single-stranded deoxyribonucleotides,single- or double-stranded ribonucleotides, RNA/DNA hybrids anddouble-stranded DNAs. Oligonucleotides, such as single-stranded DNAprobe oligonucleotides, are often synthesized by chemical methods, forexample using automated oligonucleotide synthesizers that arecommercially available. However, oligonucleotides can be made by avariety of other methods, including in vitro recombinant DNA-mediatedtechniques and by expression of DNAs in cells and organisms.

Methods of Monitoring Bladder Cancer Patients

The present disclosure includes methods of monitoring bladder cancerpatients involving determining the DNA methylation status of the patientand optionally further determining the presence of particular genemutations prevalent in bladder cancer. The methods may be performed, forexample, to determine a patient's relative risk of recurrence ascompared to that of bladder cancer patients as a whole. For example,patients may be undergoing active surveillance following surgicaltreatment for cancer.

In some embodiments, the methods comprise obtaining a urine sedimentsample from the patient, extracting DNA from the sample and exposing theextracted DNA to bisulfate to detect the presence of methylation inparticular genes. For example, in some embodiments, the methylationstatus of at least four genes selected from MEIS1, NKPD1, ONECUT2, KLF2,OSR1, SOX1, EOMES, DDX25, and TMEM106A and from at least one referencegene is determined. In some embodiments, presence or absence of specificmutations in one or both of FGFR3 and TERT genes is also determined. Insome embodiments, the FGFR3 mutation is a C to G substitution atchromosome position ch4:1803568 (Accession No._NM 001163213.1),resulting in an amino acid change of S to C at position 249 of theprotein. In some embodiments the TERT mutation is a G to A substitution(or a C to T substitution on the opposing strand) at chromosome positionchr5:1295228 (Accession No._NM 001193376.1), which results in a mutationin a promoter region for the TERT gene.

In some embodiments, the genes for methylation analysis comprise atleast two of, at least three of, or each of MEIS1, NKPD1, ONECUT2, andKLF2. In some embodiments, one or more of the following genes is alsoused for methylation analysis: EPHX3, IRX5, NID2, VIM, and ITPKB. Insome embodiments, one, two, three, or four or more reference genes areassessed in the methylation analysis, for example, for normalizing andthus correcting the signal from the test genes to account for the amountof DNA in the sample. In some embodiments, the reference genes includeCTNS, TOP3A, COL2A and SLC24A3.

In some embodiments, the genes for methylation analysis comprise thegenes of the M1 model described in Example 3 herein. In someembodiments, the genes for methylation analysis comprise the genes ofthe M2 model described in Example 3 herein. In some embodiments, thegenes for methylation analysis comprise the genes of the M3 modeldescribed in Example 3 herein. In some embodiments, the genes formethylation analysis comprise the genes of the M4 model described inExample 3 herein. In some embodiments, the genes for methylationanalysis comprise the genes of the M5 model described in Example 3herein. In some embodiments, the genes for methylation analysis comprisethe genes of the M6 model described in Example 3 herein. In someembodiments, the genes for methylation analysis comprise the genes ofthe M7 model described in Example 3 herein. In some embodiments, thegenes for methylation analysis comprise the genes of the M8 modeldescribed in Example 3 herein. In some embodiments, the genes formethylation analysis comprise the genes of the M9 model described inExample 3 herein.

In some embodiments, the methods herein are used alone or in combinationwith other diagnostic tests or assays to determine the relativerecurrence risk for a patient compared to data from a group of bladdercancer patients of various recurrence risks or actual levels ofrecurrence. For example, a distribution of recurrence risks can beobtained by noting the outcome of the methylation and optionalmutational assay on samples from patients with previously determinedrecurrence risks from other diagnostic tests or from archived samplesfrom patients who are known to recur or not to recur. For example, suchdata can be used to create a distribution of test results againstrecurrence risk for bladder cancer patients. Then, data from a newpatient can be compared to data from the distribution to determine ifthe patient's risk of recurrence is, for example, low or high ascompared to the overall average from the distribution, or to determinewhere the new patient falls along the distribution curve. Such analysiscan provide a relative risk of recurrence for the new patient.

In some embodiments, the patient has already been diagnosed withnon-muscle-invasive bladder cancer. In some embodiments, the patient'stumor has previously been diagnosed as Ta, Tis, T0, or low grade. Insome other embodiments, the patient's tumor has previously beendiagnosed as T1 or high grade. And in some embodiments, the patient hasbeen diagnosed with muscle-invasive bladder cancer. In some embodiments,the patient has been determined to have a low risk of recurrence on thebasis of other diagnostic and/or clinical test results. In someembodiments the method is conducted during an active surveillance visit,such as a visit prior to cystoscopy or a visit for the purpose ofdetermining whether cystoscopy should be performed, or a visit for thepurpose of determining how frequently cystoscopy should be performed.For example, if the assay herein reveals or confirms that the patientshould have a low risk of bladder cancer recurrence, cystoscopy may notbe performed directly after the assay is run, or the frequency ofcystoscopy may be reduced. If the assay herein reveals that a patientmay have a higher risk of recurrence than previously thought, cystoscopymay be performed as part of the active surveillance visit or may beperformed in a follow-up visit. In some embodiments, the methods hereinmay be conducted after a negative cystoscopy, for example, to helpconfirm a low risk of recurrence. In some embodiments, methods hereinare conducted at least once per year, such as once per six months, oronce per three months.

In some embodiments, a patient has previously been determined to have alow risk of recurrence based on a Ta or low grade tumor samplecomprising tissue from TURBT, cystoscopy, or from an upper GU workup. Insome embodiments, the low risk patient has either normal or abnormalcystoscopy. In some embodiments, the low risk patient has either normalor abnormal cytology. In some embodiments, the patient has NMIBC and hasa negative tissue analysis result from one or more of TURBT, cystoscopy,or upper GU workup and the methods herein are used to confirmnon-recurrence of bladder cancer. In some embodiments, the patient hasNMIBC and has previously had a tissue analysis result giving Ta or lowgrade cancer from one or more of TURBT, cystoscopy, or upper GU workupand the methods herein are used to confirm that the patient has a lowrisk of bladder cancer recurrence. In such methods, if the methodsherein indicate that the patient actually has a high risk of recurrenceor indicate any actual presence of recurrence, the frequency ofcystoscopy for the patient may be increased in future surveillancevisits or further tissue analysis may be performed. For example, in someembodiments, a patient has previously been determined to have a highrisk of recurrence based on a high grade, T1-T2, or Tis tumor samplecomprising tissue from TURBT, cystoscopy, or from an upper GU workup. Insome embodiments, the high risk patient has either normal or abnormalcystoscopy. In some embodiments, the high risk patient has either normalor abnormal cytology. In some embodiments, the patient has NMIBC and haspreviously had a tissue analysis result from one or more of TURBT,cystoscopy, or upper GU workup showing high grade, T1, T2, or Tis andthe methods herein are used to confirm that the patient is at high riskfor recurrence and to check for presence of any recurrence. If themethods indicate that the patient is actually at low risk of recurrence,the frequency of cystoscopy may be reduced. If the methods indicate thatthe patient is experiencing an actual recurrence, then further tissueanalysis may be performed.

In some embodiments, an abnormal cystoscopy result comprises anyabnormality requiring a follow-up TURBT, upper GU tract workup orfollow-up cystoscopy. In some embodiments, an abnormal cytology resultcomprises any abnormality leading to TURBT or upper GU tract workup orfollow-up cystoscopy.

In some embodiments, the diagnostic methods herein may be included aspart of a method of treatment, for example, for a patient otherwisedetermined to have a low risk of recurrence. For example, if the patientis confirmed to have a low risk of recurrence according to the methodsherein, the patient may continue to be treated by active surveillance.In some embodiments, the frequency of cystoscopy may be reduced. Inother embodiments, if the patient is determined to have a higher risk ofrecurrence according to the methods herein, the frequency of cystoscopymay be increased.

Methods of Assaying Gene Methylation and Mutation

Gene methylation in bladder cancer patients may be assayed from a samplefrom the patient, such as a urine sample. After collection of a urinesample, the sample may be treated, for example by centrifugation, toisolate cellular sediment from the urine sample. DNA may then beextracted from the sediment, for example, using a DNA isolation kit suchas a Qiagen AllPrep DNA/RNA MiniKit® or other type of commerciallyavailable DNA extraction kit.

After extraction of DNA, the total amount of DNA in the sample may bequantitated, for example in nanograms, and then subjected to bisulfiteconversion for methylation analysis. For example, cytosine nucleotidesin CpG dinucleotide repeats are a key target site for methylation, inwhich cytosine is converted to 5-methyl-cytosine. Addition of sodiumbisulfite to the DNA converts unmethylated cytosines to uracils whileleaving methylated cytosines unmodified. Thus, the presence ofmethylated cytosines may be detected upon amplifying a bisulfate-treatedDNA strand, for example with appropriate primers with modified sequencesto accommodate the expected uracil conversions. Methylated cytosinesstill read as cytosines when sequenced while unmethylated cytosines readas uracils. See, e.g., K. Patterson et al., J. Vis. Exp. 56: e3170 doi:10.3791/3170 (2011); R. Shapiro et al., J. Am. Chem. Soc. 92(3): 422-424(1970); H. Hayatsu et al., J. Am. Chem. Soc. 92(3): 724-6 (1970); and M.Frommer, Proc. Natl. Acad. Sci., USA 89(5); 1827-31 (1992), fordescriptions of bisulfite conversion of methylated DNA. Commercial kitssuch as EpiTect® Fast DNA Bisulfite Kit (Qiagen; EpiBeat) may be used toperform bisulfite conversion.

Following bisulfite conversion and prior to detection of methylated DNA,the targeted genes of the DNA may be pre-amplified using a polymerasecapable of amplifying GC-rich DNA regions. An example is KAPA TaqHotstart Polymerase™ (KAPA Biosystems). Preamplification may beperformed, for example, using KAPA Probe Fast™ qPCR kits (KAPABiosystems). Following preamplification, further PCR amplification, suchas by methylation specific quantitative PCR (MS-qPCR) may then beperformed, again using polymerases capable of amplifying GC-richregions, such as with a KAPA Probe Fast™ kit.

In PCR analysis, 5′-nuclease assay data are commonly initially expressedas a threshold cycle (“C_(T)”). Fluorescence values are recorded duringevery cycle and represent the amount of product amplified to that pointin the amplification reaction. The threshold cycle (C_(T)) is generallydescribed as the point when the fluorescent signal is first recorded asstatistically significant. Data may also be expressed as a crossingpoint (“Cp”). The Cp value is calculated by determining the secondderivatives of entire qPCR amplification curves and their maximum value.The Cp value represents the cycle at which the increase of fluorescenceis highest and where the logarithmic phase of a PCR amplificationbegins.

In some embodiments, qPCR may also be used to detect mutations inparticular genes or gene regulatory regions. PCR detection of mutationsmay in some embodiments be performed in the same PCR reaction asdetection of gene methylation, for example, by including primers to thegenes of interest modified to account for bisulfite treatment. Forexample, in some embodiments, mutations in genes such as FGFR3 and TERTmay also be detected. For example, in some embodiments, the FGFR3mutation found at genomic position (HG19) chr4: 1803568 is detected.That mutation involves a change of a C to a G in the DNA sequence,resulting in an S249C amino acid change in the protein. In someembodiments, the TERT mutation found at genomic position (HG19) chr5:1295228 is detected. That mutation involves a change of a G to an A inthe DNA sequence of a promoter region for the gene.

To minimize errors and the effect of sample-to-sample variation, PCR canbe performed using an internal standard. The ideal internal standardgene (also referred to as a reference gene) either does not contain anyCpG dinucleotides and so is not methylated to any higher or lower degreein a bladder cancer patient than in a non-bladder cancer patient, or forother reasons is unchanged in methylation in a cancer patient. Thus,qPCR data on a reference gene should be reflective of the amount of DNAin the sample rather than the degree of DNA methylation.

Design of PCR Primers and Probes

More generally, PCR primers and probes for genes of interest can bedesigned, for example, based upon exon or intron sequences present inthe mRNA transcript of the gene of interest or based on promoter regionson either side of a gene of interest, and based on expected sequencemodifications due to bisulfate conversion. Primer/probe design can beperformed using publicly available software, such as the DNA BLATsoftware developed by Kent, W. J., Genome Res. 12(4):656-64 (2002), orby the BLAST software including its variations.

Where necessary or desired, repetitive sequences of the target sequencecan be masked to mitigate non-specific signals. Exemplary tools toaccomplish this include the Repeat Masker program available on-linethrough the Baylor College of Medicine, which screens DNA sequencesagainst a library of repetitive elements and returns a query sequence inwhich the repetitive elements are masked. The masked intron sequencescan then be used to design primer and probe sequences using anycommercially or otherwise publicly available primer/probe designpackages, such as Primer Express (Applied Biosystems); MGBassay-by-design (Applied Biosystems); Primer3 (Steve Rozen and Helen J.Skaletsky (2000) Primer3 on the WWW for general users and for biologistprogrammers. (See S. Rrawetz, S. Misener, Bioinformatics Methods andProtocols: Methods in Molecular Biology, pp. 365-386 (Humana Press).)

Other factors that can influence PCR primer design include primerlength, melting temperature (Tm), and G/C content, specificity,complementary primer sequences, and 3′-end sequence. In general, optimalPCR primers are generally 17-30 bases in length, and contain about20-80%, such as, for example, about 50-60% G+C bases, and exhibit Tm'sbetween 50 and 80° C., e.g. about 50 to 70° C. In some embodiments, PCRprimers or blocking nucleic acids may comprise DNA or may comprise DNAanalogs such as locked nucleic acids (LNA) or protein nucleic acids(PNA).

For further guidelines for PCR primer and probe design see, e.g.Dieffenbach, C W. et al, “General Concepts for PCR Primer Design” in:PCR Primer, A Laboratory Manual, Cold Spring Harbor Laboratory Press.New York, 1995, pp. 133-155; Innis and Gelfand, “Optimization of PCRs”in: PCR Protocols, A Guide to Methods and Applications, CRC Press,London, 1994, pp. 5-11; and Plasterer, T. N. Primerselect: Primer andprobe design. Methods Mol. Biol. 70:520-527 (1997), the entiredisclosures of which are hereby expressly incorporated by reference.

Algorithms and Statistical Analysis Methods

The methylation fraction of the genes to be analyzed, in someembodiments, is determined using real-time or quantitative PCRtechnology. For example, a crossing point or Cp value may represent thePCR cycle at which, for instance, methylation of a particular gene isfirst detected in a patient's sample. This information can be comparedto a previously obtained standard curve of Cp value versus log 10concentration for the methylated gene and then normalized to a similarvalue for a reference gene, in order to obtain a methylation fractionfor that gene. This is illustrated in the equations below, in which mrepresents data from the test gene and a represents data from amethylation-independent (MIP) reference gene:

$m = \frac{{C_{p}({gene})} - {{intercept}({gene})}}{{slope}({gene})}$$\alpha = \frac{{C_{p}({MIP})} - {MIPintercept}}{MIPslope}$Methylation  Fraction = 2^(m − α)

First, a standard curve plotting, for example, Cp against log 10 geneconcentration may be determined for each gene by taking a DNA sample ofknown concentration representing a PCR amplification product for themethylated or unmethylated version of the gene and performing a set ofserial dilutions and measuring the fluorescence signal at eachconcentration and relating that fluorescence signal to a PCR cycle valuecorresponding to that signal. Such a Cp versus log 10 concentrationcurve may be linear over several orders of magnitude, allowing a slopeand intercept to be determined (see the equations above). (See, e.g., D.Rodriguez-Lazaro & M. Hernandez, Introduction to the Real-Time PCR, inReal-Time PCR in Food Science: Current Technology and Applications, D.Rodriguez-Lazaro Ed., Caister Academic Press, Norfolk, UK (2013) fordescription of how to obtain and utilize such a standard curve.)

For example, a 15-point linearity analysis to obtain such a slope andintercept may be performed by taking a nucleic acid of knownconcentration and performing 14 two-fold serial dilutions and alsoobtaining a 15^(th) sample comprising no nucleic acid. Expression of theassociated gene in each of the 15 samples is then determined byconducting PCR on each sample and determining Cp value, with the Cpvalue for the no nucleic acid sample expected to equal the number of PCRcycles (e.g. 40). If the PCR reaction is perfect, then there should bean increase of Cp value by 1 for each two-fold dilution. The observed Cpvalues are plotted against the known starting concentration of thenucleic acid and the slope of the resulting line and its intercept arecalculated.

Once the slope and intercept for this standard curve (and a similarstandard curve for one or more reference genes) have been obtained, theymay be used to calculate m and a values and subsequently, themethylation fraction (MF) for the test gene (see equations above). Toensure that the MF value falls between zero and one, the MF in someembodiments may be equal to the maximum of (a) zero or (b) the valuecorresponding to the minimum of 1 or 2^(m−a). This may be expressed as:MF=max(0, min(1, 2^(m−a))).

The MF for each gene of interest may then be used to calculate othervalues. In some embodiments, an amount of methylated DNA, equal to theMF multiplied by the DNA yield of the sample (for example, in nanograms)is also determined for each gene. In some embodiments, the amount ofmethylated DNA may reflect how much tumor-related DNA is actually“leaking” into a patient's urine. And in other embodiments, theconcentration of methylated DNA is determined, which is equal to theamount of methylated DNA divided by the sample volume (for example, inmilliliters). The concentration of methylated DNA may correct for theconcentration of tumor-related DNA in the sample that a test laboratoryreceives, for example, and so may not be affected in case only a portionof a urine sample is received by the laboratory. However, this value maybe affected by the amount of liquid the patient consumes prior to urinecollection. In some embodiments, both the amount and the concentrationof methylated DNA may be determined for a gene.

In some embodiments, a mean amount or concentration of methylated DNA isthen determined for a set of genes being assayed. For example, in someembodiments, at least four, at least five, or at least six test genesare assayed. In such cases, a mean amount or mean concentration ofmethylated DNA may be reported in an algorithm herein. In someembodiments, the amount and/or concentration of methylated DNA may becalculated for each gene of a set of genes and then the values averaged.

In some embodiments, gene mutations are also assessed. In someembodiments, such mutations may also be assessed using real-time orquantitative PCR methods. Thus, for example, a mutation may beconsidered to be present if a fluorescence signal associated with anamplification product for that mutation is detected by a particularcrossing point in a PCR amplification. In some embodiments, for example,mutations in FGFR3 and/or TERT are also included in the assays herein.In some embodiments, the FGFR3 mutation is at chromosome position chr4:1803568, which changes a C to a G, resulting in an S249C mutation inthe protein. In some embodiments, the TERT mutation is at chromosomeposition chr5:1295228, which changes a G to an A, resulting in amutation in a promoter region.

In some embodiments, an algorithm may be developed from the mean amountor concentration of methylated DNA from the test genes and optionallyalso from the presence or absence of the FGFR3 and/or TERT mutations. Insome embodiments, the algorithm may involve the following set of steps:

-   -   1) For each methylation marker gene and reference gene, correct        for differential PCR amplification efficiency using the        intercept and slope estimated from an associated standard curve,        as discussed above.    -   2) Calculate them and a values of the marker and reference        genes, where an average a value is used in the methylation        fraction equation when more than one reference gene is included        in the analysis    -   3) Estimate Methylation Fraction for each gene as MF=max (0,(min        1, 2^(m−a))    -   4) Apply selected method (a) or (b) to quantify methylation of        individual genes:        -   a. Amount of methylated DNA amount=MF*Sample yield (ng), or        -   b. Concentration of methylated DNA conc=MF*Sample yield            (ng)/Sample Volume (mL)    -   5) Calculate Log 10 (average amount or conc across the genes)    -   6) Optionally, also define presence of mutations in FGFR3 and        TERT        In some embodiments, a threshold Cp value for presence of each        mutation is defined as the number of cycles by which        fluorescence signal associated with the mutation must appear for        a mutation to be called as being present. To evaluate        performance of the final algorithms, in some embodiments, a        multinomial logistic regression model with categories of HR        recurrence, LR recurrence and no recurrence was fit with the        following predictor variables:    -   2 degrees of freedom natural cubic spline to the method of        choice:        -   Log 10 (average amount), or        -   Log 10 (average conc.)    -   Indicators for FGFR3 and TERT mutations, if included in the        assay    -   Indicator for patient subgroups (i.e., initial low risk of        recurrence (“initial LR”) or initial high risk of recurrence        (“initial HR”))

From these steps, in some embodiments, regression analysis may be usedto provide a range of “scores” for bladder cancer patients, thuscreating a reference curve of risk of recurrence or actual recurrenceagainst a “score” from the algorithm against which data for a newpatient may be compared. Such a curve may then be used to determine arelative risk of recurrence, for example, for that new patient.

In some embodiments, the genes assayed in the above algorithms compriseat least 4 genes selected from MEIS1, NKPD1, ONECUT2, KLF2, OSR1, SOX1,EOMES, DDX25, and TMEM106A and optionally further genes selected fromEPHX3, IRX5, NID2, VIM, and ITPKB) and one or more reference genes. Insome embodiments, the genes assayed comprise each of MEIS1, NKPD1,ONECUT2, and KLF2. In some embodiments, the genes assayed in the abovealgorithms comprise or consist of those of the M1 gene set listed inExample 3 below along with one or more reference genes. In someembodiments, the genes assayed in the above algorithms comprise orconsist of those of the M2 gene set listed in Example 3 below along withone or more reference genes. In some embodiments, the genes assayed inthe above algorithms comprise or consist of those of the M3 gene setlisted in Example 3 below along with one or more reference genes. Insome embodiments, the genes assayed in the above algorithms comprise orconsist of those of the M4 gene set listed in Example 3 below along withone or more reference genes. In some embodiments, the genes assayed inthe above algorithms comprise or consist of those of the M5 gene setlisted in Example 3 below along with one or more reference genes. Insome embodiments, the genes assayed in the above algorithms comprise orconsist of those of the M6 gene set listed in Example 3 below along withone or more reference genes. In some embodiments, the genes assayed inthe above algorithms comprise or consist of those of the M7 gene setlisted in Example 3 below along with one or more reference genes. Insome embodiments, the genes assayed in the above algorithms comprise orconsist of those of the M8 gene set listed in Example 3 below along withone or more reference genes. In some embodiments, the genes assayed inthe above algorithms comprise or consist of those of the M9 gene setlisted in Example 3 below along with one or more reference genes. Insome embodiments, the reference genes comprise one or more of CTNS,TOP3A, COL2A, and SLC24A3.

In some embodiments, the performance of an algorithm may be monitored,for example, in a test population with known clinical outcomes, byassessing negative predictive value (NPV), sensitivity, positivepredictive value (PPV), and specificity. For example, for patientsinitially diagnosed as having a low risk recurrence (LR), the methodsherein may be used to confirm a LR diagnosis. In such a case, the NPVmay measure how accurately the methods herein correctly predict anegative result, i.e. that the patient does not have a high risk ofrecurrence. The sensitivity may reflect, for example, the percentage oftruly positive results that are correctly predicted by the test, i.e.that are true positives and not false positives. These measurements, forexample, may indicate the accuracy of the test in confirming arecurrence diagnosis. Similarly, in a test used on patients initiallydiagnosed with a high risk recurrence (HR), the methods may be used toconfirm that no recurrences are likely to occur. The PPV andspecificity, in some embodiments, may also be determined in order toassess how accurately the test predicts a positive result, and to assessthe percentage of truly negative results that are correctly predicted bythe test, respectfully. In some embodiments, a goal of the methods is toshow a high sensitivity and a high NPV (e.g. greater than 85%, such asgreater than 88%, such as greater than 90% sensitivity and greater than90%, such as greater than 95%, such as greater than 97%, such as greaterthan 98% NPV) as this might allow for reduction in the frequency ofcystoscopy for patients undergoing surveillance.

In some embodiments, further performance evaluations may be conducted,for example determining predictiveness curves, i.e. plots of estimatedprobability of a high risk of recurrence or any actual recurrenceagainst the population quantile of probability, or determining ROCcurves, i.e. plots of the sensitivity versus 1 minus the specificity ofa test, which may include cutpoints at each probability of recurrence,or determining area under an ROC curve.

One skilled in the art will recognize that there are otherwise manystatistical methods that may be used to determine whether there is asignificant relationship between an outcome of interest (e.g.,likelihood of survival, likelihood of recurrence) and a particularmarker or parameter (e.g., methylation status of a gene or presence orabsence of a genetic mutation). This relationship can be presented as acontinuous Recurrence Score® (RS), or patients may be stratified intorisk groups (e.g., low and high or low, intermediate, and high).

Kits and Systems of the Invention

The materials for use in the methods of the present invention are suitedfor preparation of kits. The present disclosure thus provides kitscomprising agents, which may include gene-specific or gene-selectiveprobes and/or primers, for quantitating the methylation and optionallymutation of the disclosed genes, for example. Kits may also includeblocking nucleic acids, for example, to block DNA repeat regions duringPCR amplifications. Such kits may optionally contain reagents for theextraction of DNA from samples and for bisulfite conversion of DNA. Inaddition, the kits may optionally comprise the reagent(s) with anidentifying description or label or instructions relating to their usein the methods of the present invention. The kits may comprisecontainers (including matrices, microtiter plates, and the like suitablefor use in an automated implementation of the method), each with one ormore of the various reagents (typically in concentrated form) utilizedin the methods, including, for example, pre-fabricated microarrays,buffers, the appropriate nucleotide triphosphates (e.g., dATP, dCTP,dGTP and dTTP), DNA polymerase, and one or more probes and primers ofthe present invention (e.g., appropriate primers for each test gene andreference gene for methylation analysis as well as for FGFR3 and TERTmutation analysis). Mathematical algorithms used to estimate or quantifyprognostic or predictive information are also potential components ofkits. Kits may also form part of a system that comprises, for example,detection devices and/or computer software for determining methylationand mutation status of a patient and for comparing patient resultsagainst those of a reference bladder cancer patient population.

In some embodiments, kits may comprise forward and reverse primers forPCR amplification of bisulfite modified genes for performing amethylation assay as described herein (e.g. at least 4 genes selectedfrom MEIS1, NKPD1, ONECUT2, KLF2, OSR1, SOX1, EOMES, DDX25, and TMEM106Aand optionally further genes selected from EPHX3, IRX5, NID2, VIM, andITPKB), and for one or more reference genes, and optionally also forevaluating FGFR3 and/or TERT mutation status. Kits may also comprise PCRreagents such as a polymerase capable of amplifying GC-rich sequences,as well as instructions for performance of the assay. Kits may also formpart of a system comprising computer software for performing themethylation and optional mutation analysis and for determining arecurrence risk for the patient.

In some embodiments, kits may comprise a cartridge comprising at leastone well (which may constitute a channel, chamber, area, or surface). Asused herein, a “cartridge” is a generic term meaning a physicalstructure that can hold reagents and that contains at least one “well.”A “well,” in turn, is a generic term meaning a channel, chamber, area,or surface that can be used to contain one or more reaction steps suchas a step of a PCR process, a DNA extraction from a sample, a wash step,or a detection step, or the like. In some embodiments, at least one wellmay comprise one or more primers for detecting methylation of genes. Thegenes may include at least 4 genes selected from MEIS1, NKPD1, ONECUT2,KLF2, OSR1, SOX1, EOMES, DDX25, and TMEM106A and optionally furthergenes selected from EPHX3, IRX5, NID2, VIM, and ITPKB and optionallyalso gene mutations, such as in TERT or FGFR3. In some embodiments,methylation is detected for each of the genes MEIS1, NKPD1, ONECUT2, andKLF2. In some embodiments, methylation is detected for the genes of theM1, M2, M3, M4, M5, M6, M7, M8, or M9 gene sets listed in Example 3below. In some embodiments, the primers are attached to one or morewells in the cartridge. In some embodiments, each well may compriseprimers for more than one gene, such as two, three, four, five or sixdifferent genes, for example, by using primers labeled with differentcolor labels. In some embodiments, at least one well comprises at leastone primer for detecting methylation of at least one reference gene. Insome embodiments, the cartridge is configured so that each wellcomprises primers for detecting methylation in one or more of the abovegenes MEIS1, NKPD1, ONECUT2, KLF2, OSR1, SOX1, EOMES, DDX25, TMEM106A,EPHX3, IRX5, NID2, VIM, and ITPKB, and further primers for detectingmethylation of at least one reference gene. In some embodiments, thereference genes comprise one or more of CTNS, TOP3A, COL2A and SLC24A3.Thus, in some embodiments, the cartridge is configured to performmethylation-specific quantitative PCR on the genes, meaning that it'sstructure and arrangement is such that it can be used for carrying out amethylation-specific quantitative PCR analysis of the genes.

In some embodiments, the cartridge further comprises amplificationreagents such as PCR enzymes, buffers, nucleotide triphosphates (e.g.dATP, dCTP, dGTP, and dTTP or rATP, rCTP, rGTP, and rUTP), and reagentsfor bisulfite conversion. In some embodiments, the cartridge is part ofa kit or system comprising one or more components that introduce thesereagents into the wells of the cartridge. In some embodiments, DNA fromthe patient sample is extracted and applied to the cartridge directly orafter bisulfite conversion. In some embodiments, extracted DNA isapplied to the cartridge and bisulfite conversion takes place in one ormore wells of the cartridge. In some embodiments, the sample is applieddirectly to the cartridge and, if DNA extraction is needed beforebisulfite conversion and amplification, extraction also takes place inone or more wells of the cartridge. In some embodiments, the sample is aurine sample, such as a urine sediment sample (for example comprisingurine cell pellets). In other embodiments, the sample is a tissuesample. Thus, in some embodiments, the cartridge is intended to accepteither urine or tissue samples. In some embodiments, the cartridge isdesigned for either urine (e.g. urine sediment) samples or tissuesamples. In some embodiments, cartridges and associated systems may bebased on those described in International Publication No. WO2006/136990and its associated U.S. Pat. No. 9,568,424.

In some embodiments, the cartridge comprises at least one wellcomprising primers where thermocycling takes place as well as one ormore wells for introducing, lysing, and/or washing the sample. In someembodiments, the overall cartridge structure also comprises pumps,valves, process wells, and fluid and waste reservoirs, which allow forconducting sample treatment and PCR reactions and associated detectionof methylation and optionally also mutation of particular genes in thecartridge.

In some embodiments the cartridge is part of a system that is capable ofquantifying methylation of certain genes from the sample using primersand reagents comprised in the cartridge, and the system also includessoftware capable of quantifying the methylation of the genes and alsocalculating values based on the methylation, such as sample yield,sample volume, methylation fraction (MF), methylated DNA amount,methylated DNA concentration, Cp values, and an overall Recurrence Score(RS) result. In some embodiments, the system also is capable of creatinga report summarizing information such as a subject's MF, methylated DNAamount, and/or methylated DNA concentration, as well as optionally theTERT and/or FGFR3 mutational status. In some embodiments, the system mayalso compare the quantitative values such as methylated DNA amountand/or methylated DNA concentration with those values taken fromreference sets of bladder cancer patients in order to predict risk ofrecurrence for the patient or detect whether the patient is having anactual recurrence.

Reports

The methods of this invention, when practiced for commercial diagnosticpurposes, generally produce a report or summary of information obtainedfrom the herein-described methods. For example, a report may includeinformation concerning amount or concentration of methylated DNA,mutation status of genes, an overall Recurrence Score result forreporting risk of recurrence in comparison to data from a reference setof bladder cancer patients, a prediction of the clinical outcome for aparticular patient, or whether the patient is above or below certainrecurrence risk thresholds. The methods and reports of this inventioncan further include storing the report in a database. The method cancreate a record in a database for the subject and populate the recordwith data. The report may be a paper report, an auditory report, or anelectronic record. The report may be displayed and/or stored on acomputing device (e.g., handheld device, desktop computer, smart device,website, etc.). It is contemplated that the report is provided to aphysician and/or the patient. The receiving of the report can furtherinclude establishing a network connection to a server computer thatincludes the data and report and requesting the data and report from theserver computer.

Computer Programs

The values from the assays described above, such as expression data,recurrence score, treatment score and/or benefit score, can becalculated and stored manually. Alternatively, the above-described stepscan be completely or partially performed by a computer program product.The present invention thus provides a computer program product includinga computer readable storage medium having a computer program stored onit. The program can, when read by a computer, execute relevantcalculations based on values obtained from analysis of one or morebiological sample from an individual. The computer program product hasstored therein a computer program for performing the calculation.

The present disclosure provides systems for executing the programdescribed above, which system generally includes: a) a central computingenvironment; b) an input device, operatively connected to the computingenvironment, to receive patient data, wherein the patient data caninclude, for example, expression level or other value obtained from anassay using a biological sample from the patient, or microarray data, asdescribed in detail above; c) an output device, connected to thecomputing environment, to provide information to a user (e.g., medicalpersonnel); and d) an algorithm executed by the central computingenvironment (e.g., a processor), where the algorithm is executed basedon the data received by the input device. The methods provided by thepresent invention may also be automated in whole or in part.

Having described the invention, the same will be more readily understoodthrough reference to the following Examples, which are provided by wayof illustration, and are not intended to limit the invention in any way.

EXAMPLES Example 1: Methods for Assessing Methylation and Proof ofConcept Study

A proof of concept (POC) study was performed using urine cell pelletsfrom 66 bladder cancer patients (25 patients with no recurrence, 14patients with low risk (LR) (Ta low grade) and 27 patients with highrisk (HR) (T1 or high grade)). Recurrence was assessed using 11methylation gene markers selected based on the literature and 1methylation-independent (MIP) reference gene. Methylation-specificquantitative PCR (MS-qPCR) analysis preceded by bisulfate conversion wasused to determine methylation levels for these 12 genes. This study wasused to evaluate the performance of the individual methylation markersand a combined score with respect to predicting the presence ofrecurrence. Several methods for evaluating methylation signal weredeveloped based on this study and utilized in a refinement study on alarger set of candidate markers.

The methylation fraction (MF) for each gene was estimated frommethylation-specific Cp as follows:

First, a linearity study was conducted for each gene by performing aserial dilution of a known concentration of PCR product fromamplification of the gene. The product was serial-diluted 2-fold 15times, and a curve of Cp value at each concentration was prepared andits slope and y-intercept obtained. Second, using the slope andintercept derived from the linearity study, a correction was applied fordifferential PCR amplification efficiency as follows:

$m = \frac{{C_{p}({gene})} - {{intercept}({gene})}}{{slope}({gene})}$$\alpha = \frac{{C_{p}({MIP})} - {MIPintercept}}{MIPslope}$

Third, Cp was normalized using the methylation-independent referencegene data, and MF was estimated according to the equation below:

Methylation Fraction=2^(m−a)

Further parameters were then obtained based on the MF, and the DNA yieldand volume of the sample, as shown in the table below:

Summary across Method Calculation per gene genes Mean MethylationFraction × Sample DNA Yield (ng) Mean Amount of Methylated DNA (ng) MeanConcentration of Methylated${Methylation}\mspace{14mu} {Fraction} \times \frac{{Sample}\mspace{14mu} {DNA}\mspace{14mu} {Yield}\mspace{11mu} ({ng})}{{Sample}\mspace{14mu} {Volume}\mspace{11mu} ({mL})}$Mean DNA (ng/mL)

A combined score based on 11 genes from the POC study was associatedwith the presence of recurrence, as shown in the table below.

Standardized Odds Method Endpoint Ratio (95% CI) p-value Mean Amount ofLR Recurrence 2.29 (0.98, 5.35) 0.052 Methylated DNA (ng) HR Recurrence5.74 (2.37, 13.90) <.001 Mean Concentration of LR Recurrence 2.03 (0.93,4.91) 0.090 Methylated DNA HR Recurrence 5.51 (2.52, 14.90) <.001(ng/mL)

Example 2: Methylome Discovery Study

A gene discovery study to identify additional gene candidates that aredifferentially methylated in samples from patients with bladder cancerand those from patients with no cancer was conducted. A total of 26tumor tissue samples and 7 urine cell pellets from patients with cancer(14 patients previously diagnosed with low risk (LR) of recurrence and19 patients previously diagnosed with high risk (HR) of recurrence) and7 urine cell pellets and 5 tissue samples from patients with no cancer(7 healthy individuals and 5 patients with no prior diagnosis of bladdercancer or with prior diagnosis but no malignancy at the time of thesurgery) were used in the analysis. Methylome assessment targetedapproximately 6 million nucleotides in regions differentially methylatedacross 11 cancers based on analysis of The Cancer Genome Atlas (TCGA)and sequenced approximately 724,000 sites for each sample. The followingassessments were conducted in this discovery study.

First, sites differentially methylated in cancer samples vs. no cancersamples were identified at 1% false discovery rate. Second, strongestcandidates for PCR assays for the refinement study were selected basedon: (a) regions with strong methylation signal, (b) high coverage, i.e.high number of CpG sites, (c) low signal from non-cancer samples, (d)distribution and location of CpG sites across primers/probes, and (e)areas of consistent methylation signal. Third, based on the findings ofthe methylome discovery study, 86 candidate methylation markers weretaken in the refinement study. This set of 86 candidates included the 11genes used in the proof of concept study of Example 1.

Example 3: Refinement Study

The refinement study assessed 96 markers including 86 methylation markergenes from the gene discovery study, 4 methylation independent referencegenes and 6 FGFR3 mutations on 203 patients with previous diagnosis ofbladder cancer who are currently on surveillance. Two of the 6 FGFR3mutation assays failed to produce any signal and were excluded fromfurther assessments. In addition, 170 patients in this study had enoughRNA left for additional assessments, including assaying 2 TERTmutations. The results presented here are based on these 170 patients inwhich 86 methylation markers, 4 reference genes, 4 FGFR3 and 2 TERTmutations were assessed. The patients included 85 who have not hadrecurrences, 32 previously diagnosed to have LR recurrence and 53previously diagnosed to have HR recurrence.

RT-PCR analysis preceded by bisulfate conversion was used to determinemethylation levels. The performance of the individual methylationmarkers was assessed using non-parametric estimates of sensitivity,specificity, negative predictive value (NPV) and positive predictivevalue (PPV). In this analysis, a positive event for purposes ofsensitivity, specificity, NPV, and PPV for a LR recurrence subject wasconsidered to be a prediction of HR recurrence. A positive event for aHR recurrence subject was considered to be evidence of any actualrecurrence.

Selection of genes for the final models was based on multipleconsiderations, including (1) representation of biological pathways, (2)strong individual gene performance with respect to NPV for predicting HRrecurrences, (3) consistent selection using elastic net penalizedregression models (Zhou H, Hastie T. Regularization and variableselection via the elastic net. Journal of the Royal Statistical Society,Series B 67:301-320) for both HR and any recurrence endpoints with andwithout mutations forced in, (4) strong performance in LASSO penalizedregression models (Tibshirani R. Regression shrinkage and selection viathe LASSO. Journal of the Royal Statistical Society, Series B58:267-288), and (5) inclusion into POC study. Two main biologicalpathways were identified: the homeobox functional group was heavilyrepresented (e.g. IRX5, MEIS1, ONECUT2, OTP, POU4F2, SIM2, SOX1), andthe immune group (KLF2, IRAK3, ITPKB).

Elastic net analyses identified several genes consistently selected forinclusion in the models with and without including the FGFR3 and TERTmutation assays: KLF2, MEIS1, DDX25, NKPD1, ONECUT2 and TMEM106A. Thefollowing table presents genes selected using elastic net analyses.

HR recurrence vs No Recurrence Any recurrence vs No RecurrenceMethylation assays + Methylation assays + Methylation assays Mutationsforced in Methylation assays Mutations forced in KLF2 KLF2 KLF2 KLF2MEIS1 MEIS1 MEIS1 MEIS1 DDX25 DDX25 DDX25 NKPD1 NKPD1 NKPD1 ONECUT2ONECUT2 ONECUT2 TMEM106A TMEM106A TMEM106A EPHX3 EPHX3 NPR3 SEPT9 P8335TRPS1_promoter2kb IRX5 IRX5 RNF219_AS1 OSR1 OSR1 OTP KLF1 DKFZp686K1684TERT TERT FGFR3 FGFR3

Thirteen methylation markers selected based on the criteria outlinedabove were considered for inclusion in the final models. Ninecombinations of these methylation markers are described in the tablebelow and were included in final models M1-M9.

Methylation Assays M1 M2 M3 M4 M5 M6 M7 M8 M9 MEIS1* X X X X X X XNKPD1† X X X X X X X ONECUT2†^(POC) X X X X X X X X KLF2*‡^(b) X X X X XOSR1‡¥^(POC) X X X X SOX1†^(POC) X X X X EOMES^(POC) X X TMEM106A†‡ X XX X EPHX3‡ X IRX5¥ X NID2^(POC) X VIM^(POC) X ITPKB^(b) X

Genes included in the POC study are noted “POC” in the table above. Thesymbol * indicates consistent entry into elastic net-selected models forboth HR and Any Rec, with and without mutations. The symbol † indicatesstrong performance for NPV as individual gene. The symbol ‡ indicatesstrong performance in LASSO models. The symbol ¥ indicates entry intoany recurrence elastic net models. The symbol ^(b) represents immunepathway gene.

In addition to methylation markers, two mutation markers were selectedfor inclusion. The first is in FGFR3, comprising a C to G mutation atgenomic position ch4:1803568, which results in an S249C mutation in theprotein. This mutation is found in greater than 65% of patients with lowrisk, low grade bladder cancer. The limit of detection for this mutationis where Cp=33. The second is in TERT at genomic position chr5:1295228,which is the most prevalent TERT mutation and is found in the promoterregion for the gene. The mutation is either a G to A change on one DNAstrand or a C to T change on the opposing strand. It is found in about65% of all bladder cancer patients. The limit of detection for thismutation is where Cp=30. The association signals for presence of each ofthese mutations with risk of recurrence in low risk and high riskpatients is shown in the table below.

Proportion of patients with detected mutations by recurrence status NoLow Risk High Risk Mutation Recurrence Recurrence Recurrence FGFR3  7%25% 21% TERT 39% 50% 68%

Addition of the FGFR3 mutation substantially improved prediction of LRrecurrence while inclusion of the TERT mutation slightly improvedperformance for HR recurrence.

Up to four methylation-independent (MIP) reference genes were used andperformed well in the linearity studies. The final 2 MIP reference genesCOL2A and SLC24A3 were selected based on performance of the modelincluding M1 methylation markers, FGFR3 and TERT mutation assays underfour different normalization schemes. No detriment in performance wasobserved when the number of MIP reference genes was reduced from 4 to 2.Methylation signal was therefore normalized relative to the average of 2genes: COL2A and SLC24A3.

Performance of each algorithm was evaluated using a multinomial logisticregression model with outcome categories of HR recurrence, LR recurrenceand no recurrence, and methylation markers, indicators for the presenceof mutations and an indicator variable for initial tumor status (lowrisk vs high risk) as predictors. Based on this model, the probabilityof {HR recurrence} and the probability of {LR recurrence} were estimatedfor every patient in the study population. NPV, PPV, sensitivity andspecificity were assessed using model-based estimates of recurrenceprobabilities, the weighted empirical distribution of predictors in thesubgroup (initial high risk or low risk tumor), and the assumed truerecurrence prevalence in the target population subgroup, specifically:initial LR tumor patients: 85% no recurrence, 10% LR recurrence, 5% HRrecurrence and initial HR tumor patients: 85% no recurrence, 2% LRrecurrence, 13% HR recurrence.

Cutpoints for defining presence of HR recurrence were determined at aspecific percentiles of the distribution of Pr{HR recurrence} in initialLR patients, for example the 50^(th) percentile. Cutpoints for definingpresence of any recurrence were determined at a prespecified percentileof the distribution of Pr{Any recurrence} in initial HR patients, forexample the 85^(th) percentile.

The final algorithms M1-M9 include the following steps:

-   -   7) For each of the methylation markers and reference genes,        correct for differential PCR amplification efficiency using the        intercept and slope estimated from a linearity study:

$m = \frac{{C_{p}({gene})} - {{intercept}({gene})}}{{slope}({gene})}$$\alpha = \frac{{C_{p}({MIP})} - {MIPintercept}}{MIPslope}$

-   -   8) Calculate average of reference genes mip=average a value of        the reference genes    -   9) Estimate Methylation Fraction for each gene as MF=the maximum        of 0 or of (the minimum of 1 or 2^(m−mip), i.e. max) 0(min 1,        2^(n−mip))    -   10) Apply selected method (a) or (b) to quantify methylation of        individual genes:        -   a. Amount of methylated DNA amount=MF*Sample yield (ng), or        -   b. Concentration of methylated DNA conc=MF*Sample yield            (ng)/Sample Volume (mL)    -   11) Calculate Log 10 (average amount or conc across the genes)    -   12) Define presence of mutations in FGFR3 at <33 Cp or TERT at        <30 Cp        To evaluate performance of the final algorithms, a multinomial        logistic regression model with categories of HR recurrence, LR        recurrence and no recurrence was fit with the following        predictor variables:    -   2 degrees of freedom natural cubic spline to the method of        choice:        -   Log 10 (average amount), or        -   Log 10 (average conc)    -   Indicators for FGFR3 and TERT mutations    -   Indicator for patient subgroup (initial LR vs initial HR        recurrence).

The performance parameters sensitivity, specificity, negative predictivevalue (NPV), and positive predictive value (PPV) were estimated usingthe fitted logistic regression models and assumed true population ratesof high risk recurrence and any recurrence for cutoffs ranging from thelowest to the highest estimated recurrence probabilities. (See FIGS.1-2, 4-5, 7-8, and 10-11 and the Tables below.) Based on the literatureand internal studies, in this analysis the following estimates of truepopulation rates of recurrence were used:

-   -   1) For initial low risk patients: 85% of patients with no        recurrence, 10% of patients with low risk recurrence and 5% of        patients with high-risk recurrence    -   2) For initial high-risk patients: 85% of patients with no        recurrence, 2% of patients with low risk recurrence and 13% of        patients with high-risk recurrence

The performance parameters were plotted against the estimated populationquantiles of these probabilities. Additional performance evaluationsincluded (see FIGS. 3, 6, 9, and 12):

-   -   1. Predictiveness curves, that is, plots of the estimated        probability of HR recurrence or any recurrence against the        population quantile of probability    -   2. ROC curves, that is, plots of the sensitivity versus 1 minus        the specificity of a test with cutpoint at each probability of        recurrence, and    -   3. The area under the ROC curve.

Performance of the final models is described in the following tables andin FIGS. 1-12. Model performance of M2-M9 gene combinations issummarized at the percentile cutpoints in the refinement study in thetables below. Specifically, subjects were divided into those with aninitial diagnosis of LR recurrence or HR recurrence. For initial LRrecurrence subjects, a positive event was considered to be the subjectactually falling into the HR recurrence category (endpoint: HRrecurrence). For initial HR recurrence subjects, a positive event wasconsidered to be any actual recurrence (endpoint: any recurrence). Thetable below provides the sensitivity, specificity, NPV, and PPVassociated with either HR recurrence in an initial LR subject or anyrecurrence in an initial HR subject provided at the 50^(th) and 85^(th)percentiles of the test population.

A detailed summary of the performance for the M1 model with the FGFR3and TERT mutation assays for the amount of methylated DNA andconcentration of the methylated DNA is also shown in FIGS. 1-12. Forexample, FIG. 1 shows the NPV, PPV, sensitivity and specificity curvesfor initial LR subjects and the NPV, PPV, sensitivity, and specificityvalues at the 50^(th) and 75^(th) percentiles of the subjects, where apositive event is considered to be an actual HR of recurrence. FIG. 2shows the NPV, PPV, sensitivity and specificity curves for initial LRsubjects and the NPV, PPV, sensitivity, and specificity values at the50^(th) and 75^(th) percentiles of the subjects, where a positive eventis considered to be any actual recurrence. Related predictiveness andROC curves are shown in FIG. 3. Similar data for initial HR patients isshown in FIGS. 4-6. Data based on analysis of the concentration ofmethylated DNA is shown in FIGS. 7-12.

Performance of the Final Algorithms M1-M9 for the Amount of MethylatedDNA at Percentile Cutpoints Based on the Refinement Study

Initial Model Recurrence Endpoint Percentile Sensitivity Specificity NPVPPV M1 LR HR recurrence 0.50 96.5% 52.4% 99.7% 9.5% 0.85 69.1% 87.8%98.2% 22.6% HR Any recurrence 0.50 91.1% 57.1% 97.4% 26.8% 0.85 45.7%90.3% 90.6% 44.8% M2 LR HR recurrence 0.50 96.0% 52.4% 99.6% 9.6% 0.8565.2% 87.6% 98.0% 21.9% HR Any recurrence 0.50 90.6% 57.0% 97.3% 26.6%0.85 46.0% 90.3% 90.7% 45.0% M3 LR HR recurrence 0.50 96.2% 52.4% 99.6%9.5% 0.85 67.0% 87.7% 98.1% 22.2% HR Any recurrence 0.50 89.9% 56.9%97.1% 26.4% 0.85 45.7% 90.3% 90.6% 44.7% M4 LR HR recurrence 0.50 94.2%52.3% 99.4% 9.1% 0.85 61.1% 87.3% 97.8% 19.8% HR Any recurrence 0.5090.1% 56.9% 97.1% 26.6% 0.85 44.9% 90.2% 90.5% 44.1% M5 LR HR recurrence0.50 95.9% 52.4% 99.6% 9.6% 0.85 67.0% 87.7% 98.1% 22.3% HR Anyrecurrence 0.50 90.8% 57.0% 97.3% 26.7% 0.85 45.9% 90.3% 90.6% 45.0% M6LR HR recurrence 0.50 96.0% 52.4% 99.6% 9.6% 0.85 68.5% 87.8% 98.1%22.9% HR Any recurrence 0.50 90.6% 57.0% 97.2% 26.6% 0.85 46.5% 90.4%90.8% 45.5% M7 LR HR recurrence 0.50 96.0% 52.4% 99.6% 9.5% 0.85 68.6%87.8% 98.2% 22.5% HR Any recurrence 0.50 90.4% 57.0% 97.2% 26.6% 0.8545.4% 90.2% 90.5% 44.5% M8 LR HR recurrence 0.50 93.9% 52.2% 99.4% 8.9%0.85 60.7% 87.3% 97.8% 19.4% HR Any recurrence 0.50 90.0% 56.9% 97.0%26.6% 0.85 44.8% 90.2% 90.4% 44.2% M9 LR HR recurrence 0.50 96.3% 52.4%99.6% 9.5% 0.85 69.8% 87.9% 98.2% 23.0% HR Any recurrence 0.50 91.0%57.1% 97.4% 26.8% 0.85 45.2% 90.2% 90.5% 44.4%

Performance of the Final Algorithms M1-M9 for the Concentration ofMethylated DNA at Percentile Cutpoints Based on the Refinement Study

Initial Model Recurrence Endpoint Percentile Sensitivity Specificity NPVPPV M1 LR HR recurrence 0.50 93.1% 52.4% 99.3% 9.7% 0.85 69.0% 88.0%98.1% 24.0% HR Any recurrence 0.50 89.0% 56.6% 96.8% 25.8% 0.85 43.8%89.9% 90.4% 42.4% M2 LR HR recurrence 0.50 93.1% 52.4% 99.3% 9.8% 0.8566.7% 87.9% 97.9% 23.4% HR Any recurrence 0.50 88.5% 56.5% 96.7% 25.6%0.85 44.3% 90.0% 90.5% 42.8% M3 LR HR recurrence 0.50 92.7% 52.4% 99.2%9.7% 0.85 66.7% 87.8% 98.0% 23.3% HR Any recurrence 0.50 88.1% 56.4%96.5% 25.5% 0.85 43.4% 89.8% 90.4% 41.8% M4 LR HR recurrence 0.50 91.6%52.2% 99.1% 9.2% 0.85 62.8% 87.5% 97.8% 21.2% HR Any recurrence 0.5087.8% 56.4% 96.4% 25.6% 0.85 43.7% 89.9% 90.3% 42.4% M5 LR HR recurrence0.50 93.0% 52.4% 99.3% 9.8% 0.85 68.1% 88.0% 98.0% 24.1% HR Anyrecurrence 0.50 88.8% 56.6% 96.8% 25.8% 0.85 44.4% 90.0% 90.5% 43.0% M6LR HR recurrence 0.50 93.1% 52.4% 99.3% 9.9% 0.85 68.7% 88.0% 98.0%24.4% HR Any recurrence 0.50 88.6% 56.5% 96.7% 25.6% 0.85 44.4% 90.0%90.5% 42.8% M7 LR HR recurrence 0.50 92.7% 52.4% 99.2% 9.7% 0.85 68.1%87.9% 98.0% 23.9% HR Any recurrence 0.50 88.5% 56.5% 96.7% 25.7% 0.8543.7% 89.9% 90.4% 42.2% M8 LR HR recurrence 0.50 91.2% 52.2% 99.1% 9.1%0.85 62.3% 87.5% 97.8% 20.6% HR Any recurrence 0.50 87.6% 56.4% 96.4%25.6% 0.85 43.5% 89.9% 90.3% 42.4% M9 LR HR recurrence 0.50 92.9% 52.4%99.2% 9.8% 0.85 69.6% 88.0% 98.1% 24.5% HR Any recurrence 0.50 88.8%56.6% 96.8% 25.8% 0.85 43.7% 89.9% 90.4% 42.2%

Example 4: Methods for Methylation and Mutation Detection

The following methods have been used for methylation and mutationdetection, according to the flow diagram shown in FIG. 13. A QiagenDNA/RNA MiniKit® was used for DNA extraction from centrifuged urinesamples. Bisulfite conversion of the DNA was performed using the QiagenEpiTect® Fast DNA Bisulfite Kit. Preamplification of the targeted genesand methylation-specific quantitative PCR (MS-qPCR) were performed usinga KAPA Probe Fast® kit (KAPA Biosystems).

In the assays, methylation and FGFR3/TERT mutation status were detectedin a single workflow assay. This was enabled by bisulfate converting thecell pellet DNA such that methylated cytosines remain cytosines whileun-methylated cytosines become uracils, performing a limited number ofPCR cycles using methylation specific primers as well as mutationspecific primers with wild-type blocking nucleic acids. All of theprimers were designed to amplify bisulfate-converted DNA, to anneal tobisulfate-converted templates. This was followed by single-plexquantitative PCR analysis of methylation and mutations on the amplifiedproducts to determine the methylation and mutation status of the desiredsites. Certain aspects of this methodology are described in J. Morlan etal., PLoS One Vol. 4, Issue 2, e4584, pp 1-11 (2009).

Example 5: Validation Study

A clinical validation study of a urine-based assay with genomic andepigenomic markers for predicting recurrence is conducted in non-muscleinvasive bladder cancer (NMIBC) subjects. The study will involve about30 clinical sites and will seek to enroll all patients with an abnormalcystoscopy and cytology at those sites, up to about 380 total patients.Enrollment of 380 patients is expected to yield about 300 usable patientsamples for analysis. Specifically, the inclusion criteria are patientswith prior diagnosis of non-muscle invasive, T1 or lower, urothelialcell carcinoma of the bladder who are scheduled to undergo surveillancecystoscopy, where the initial diagnosis or most recent recurrence ofNMIBC is within the last 5 years. Patients may be excluded if they havemuscle invasive disease or are T2 or higher or do not have an availabletumor block from their initial diagnosis or most recent recurrence, orwho are contraindicated for certain clinical procedures including TURBT.

The primary objective of the study is to validate association betweenlikelihood of recurrence and score results from the methylation assay.The study will also characterize the sensitivity, specificity, NPV andPPV of the assay in this group of patients. Urine samples are collectedafter first morning void and before any manipulation of the bladder(e.g. cystoscopy, surgery, catheterization) and at least 1 day after aprior manipulation. Archived, fixed, embedded (FPE) tissue samples arealso collected from tumor tissue specimens obtained from TURBT, upper GUtract workup, or cystoscopies. The patient's initial cystoscopy,cytology (if available), TURBT or cystoscopy tissue analysis, and upperGU workup are provided. Cystoscopy and cytology are recorded as eithernormal or abnormal, and tissue from TURBT and upper GU tract workup arerecorded as negative, or as Ta low grade, or as high grade, T1-T2, Tis.For purposes of this study, an abnormal cystoscopy result is anyabnormality observed that requires a follow-up TURBT, upper GU tractworkup or follow-up cystoscopy. An abnormal cytology result is also anyabnormality leading to TURBT or upper GU tract workup or follow-upcystoscopy. A positive upper GU tract workup is defined ashistologically confirmed upper GU tract urothelial carcinoma while anegative upper GU tract workup is defined as no clinical evidence of anupper GU tract urothelial carcinoma.

The primary clinical endpoint that the test is used to validate ispresence of recurrence based on the patient's initial cystoscopy,cytology (if available), TURBT or cystoscopy tissue analysis, and upperGU workup. Generally, patients with a negative tissue analysis resultfrom TURBT or cystoscopy and/or a negative tissue analysis from upper GUtract workup are tested to check that there is no recurrence of cancer.Patients with Ta low grade tissue results from tissue analysis of eitherTURBT/cystoscopy and/or upper GU tract workup are tested to check thatthe risk of recurrence is low. Patients with high grade, T1-T2, Tistissue results from tissue analysis of either TURBT/cystoscopy or upperGU tract workup are tested to check that the risk of recurrence remainshigh.

Methylation levels of gene markers are measured by quantitative PCR inboth FPE samples and urine samples. For each methylation marker, a Cpmeasurement is obtained and the measurement is normalized relative to amethylation-independent assay from a reference marker. The methylationfraction (MF) from each sample for each gene is calculated as describedelsewhere herein.

Statistical analysis will involve about 300 NMIBC patients with bladdercancer methylation analysis results obtained from urine and FPE tissuesamples. To obtain this number of samples, the study will enroll about380 patients. To determine whether there is a significant relationshipbetween the likelihood of recurrence and the methylation analysisresult, the analysis will fit a multinomial logistic regression model asdescribed in Example 3 above. A p-value of <0.05 for the likelihoodratio test between the null model excluding the bladder cancer scoreresult and the full model including the bladder cancer score result willbe considered significant.

The performance of the methylation analysis at specified cutpoints willbe characterized in terms of sensitivity, specificity, negativepredictive value (NPV), and positive predictive value (PPV) forpredicting any recurrence and separately for predicting high risk ofrecurrence. These parameters will be calculated based on the logisticregression model noted above with estimates of population prevalence ofhigh risk and low risk recurrence derived from the population screenedfor study entry.

Multivariate models including bladder cancer methylation analysis andexisting risk scoring systems will also be considered, including modelswith the methylation analysis result and the EORTC, CUETO, and EAUrecurrence risk or risk score groups. (See, respectively, R. J.Sylvester et al., Eur. Urol. 49: 466-75 (2006); J. Fernandez-Gomez etal., J. Urol. 182: 2195-203 (2009); and NCCN Clinical PracticeGuidelines in Oncology Bladder Cancer version 2.2015, available at www“dot” nccn “dot” org “/” professionals “/” physician_gls “/” pdf “/”bladder “dot” pdf.)

Additional analyses will include each of the following. First,sensitivity analyses using information from surveillance visits will beconducted. Specifically, patients who had no cancer recurrence in thefirst on-study surveillance visit but who had a recurrence at afollow-up surveillance visit will be considered as recurrences in theanalysis to assess the ability of the methylation assay to detect cancerrecurrence early. The performance of the methylation result atparticular cutpoint values will be characterized in terms ofsensitivity, specificity, NPV, and PPV. Second, the probability of lowrisk, high risk and any recurrence as a function of methylation analysisresult using the multinomial logistic regression model noted above willbe estimated by using estimates of population prevalence of high riskand low risk recurrence derived from the population screened for studyentry. If the assumptions underlying the logistic model do not hold,alternative techniques may be used to estimate the relationship betweenthe methylation assay result and the likelihood of recurrence andevaluate the sensitivity of the probability estimates to the choice ofmethod. The probability of low risk, high risk and any recurrence willbe estimated as a function of the methylation analysis result at variouslevels of highly prognostic covariates. Alternative functional forms ofthe association between the methylation score result and likelihood ofrecurrence using methods such as inclusion of non-linear terms orsmoothing splines into the logistic models may also be performed. Third,relationship between methylation test result and probability ofrecurrence in subgroups defined by prognostic covariates will beevaluated by fitting a multinomial logistic regression model thatincludes a methylation test result, a given covariate, and theinteraction of the two. The model may be adjusted for other highlyprognostic variables. Subgroup-specific odds ratios for recurrence maythen be reported.

What is claimed is:
 1. A method of monitoring a patient with bladder cancer, comprising: obtaining a urine sediment sample from the patient; bisulfite converting DNA from the sample; performing methylation-specific quantitative PCR on the bisulfite converted DNA to determine the amount and/or the concentration of methylated DNA in the sample from a set of genes comprising at least four genes selected from MEIS1, NKPD1, ONECUT2, KLF2, OSR1, SOX1, EOMES, DDX25, and TMEM106A and from at least one reference gene.
 2. The method of claim 1, wherein the patient has been diagnosed with non-muscle-invasive bladder cancer.
 3. The method of claim 2, wherein the patient's tumor has previously been diagnosed as Ta, Tis, T0, or low grade; or as Ta, T0, or low grade; or as Ta or low grade.
 4. The method of claim 2, wherein the patient's tumor has previously been diagnosed as T1 or high grade; or as T1, Tis, or high grade; or as T1-T2 or high grade; or as Tis, T1-T2, or high grade.
 5. The method of claim 1, wherein the patient has been diagnosed with muscle-invasive bladder cancer.
 6. The method of any one of claims 1-5, wherein the method is conducted prior to cystoscopy.
 7. The method of any one of claims 1-5, wherein the method is conducted after a negative cystoscopy.
 8. The method of any one of claims 1-7, wherein the method is conducted at least once per year, such as once per six months, or once per three months.
 9. The method of any one of claims 1-8, wherein the at least four genes comprise MEIS1, NKPD1, ONECUT2, and KLF2.
 10. The method of any one of claims 1-9, wherein the at least one reference gene comprises one or more of CTNS, TOP3A, COL2A and SLC24A3.
 11. The method of any one of claims 1-10, wherein the set of genes further comprises at least one gene selected from EPHX3, IRX5, NID2, VIM, and ITPKB.
 12. The method of any one of claim 1-8 or 10-11, wherein the set of genes comprises one of the following gene sets: (a) M1: MEIS1, NKPD1, ONECUT2, KLF2; (b) M2: MEIS1, NKPD1, ONECUT2, OSR1; (c) M3: MEIS1, NKPD1, KLF2, SOX1; (d) M4: ONECUT2, OSR1, SOX1, EOMES; (e) M5: MEIS1, NKPD1, ONECUT2, OSR1, TMEM106A; EPHX3; (f) M6: MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, IRX5; (g) M7: MEIS1, NKPD1, ONECUT2, KLF2, SOX1, TMEM106A; (h) M8: ONECUT2, OSR1, SOX1, EOMES, NID2, VIM; and (i) M9: MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, ITPKB.
 13. The method of any one of claim 1-8 or 10-11, wherein the set of genes consists of one of the following gene sets: (a) M1: MEIS1, NKPD1, ONECUT2, KLF2; (b) M2: MEIS1, NKPD1, ONECUT2, OSR1; (c) M3: MEIS1, NKPD1, KLF2, SOX1; (d) M4: ONECUT2, OSR1, SOX1, EOMES; (e) M5: MEIS1, NKPD1, ONECUT2, OSR1, TMEM106A; EPHX3; (f) M6: MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, IRX5; (g) M7: MEIS1, NKPD1, ONECUT2, KLF2, SOX1, TMEM106A; (h) M8: ONECUT2, OSR1, SOX1, EOMES, NID2, VIM; and (i) M9: MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, ITPKB; and at least one reference gene.
 14. The method of any one of claims 1-13, wherein the method further comprises determining a risk of recurrence for the patient, comprising comparing the amount and/or concentration of methylation for the patient to the amount and/or concentration of methylation for a reference set of bladder cancer patients.
 15. The method of any one of claims 1-14, wherein the method further comprises analyzing the bisulfate converted DNA for mutations in one or both of FGFR3 and TERT.
 16. The method of claim 15, wherein the FGFR3 mutation is FGFR3 is a C to G substitution at chr4:1803568 and wherein the TERT mutation is a G to A substitution at chr5:1295228.
 17. The method of claim 15 or 16, wherein the method further comprises comparing the presence or absence of mutations in FGFR3 and/or TERT to that of the reference set of bladder cancer patients.
 18. A method of treating a bladder cancer patient having a low risk of recurrence, comprising: obtaining a urine sediment sample from the patient; bisulfite converting DNA from the sample; performing methylation-specific quantitative PCR on the bisulfite converted DNA to determine the amount and/or the concentration of methylated DNA in the sample from a set of genes comprising at least four genes selected from MEIS1, NKPD1, ONECUT2, KLF2, OSR1, SOX1, EOMES, DDX25, and TMEM106A and from at least one reference gene; determining that the patient has a low risk of recurrence by comparing the amount and/or concentration of methylation for the patient to the amount and/or concentration of methylation for a reference set of bladder cancer patients; and treating the patient with active surveillance.
 19. The method of claim 18, wherein the treatment with active surveillance comprises reducing the frequency of cystoscopy for the patient.
 20. The method of claim 18 or 19, wherein the patient has been diagnosed with non-muscle-invasive bladder cancer.
 21. The method of claim 120 wherein the patient's tumor has previously been diagnosed as Ta, Tis, T0, or low grade; or as Ta, T0, or low grade; or as Ta or low grade.
 22. The method of claim 20, wherein the patient's tumor has previously been diagnosed as T1 or high grade; or as T1, Tis, or high grade; or as T1-T2 or high grade; or as Tis, T1-T2, or high grade.
 23. The method of any one of claims 18-22, wherein the method is conducted prior to cystoscopy.
 24. The method of any one of claims 18-22, wherein the method is conducted after a negative cystoscopy.
 25. The method of any one of claims 18-24, wherein the at least four genes comprise MEIS1, NKPD1, ONECUT2, and KLF2.
 26. The method of any one of claims 18-25, wherein the reference genes comprise CTNS, TOP3A, COL2A and SLC24A3.
 27. The method of any one of claims 18-26, wherein the set of genes further comprises at least one gene selected from EPHX3, IRX5, NID2, VIM, and ITPKB.
 28. The method of any one of claim 18-24 or 26-27, wherein the set of genes comprises one of the following gene sets: (a) M1: MEIS1, NKPD1, ONECUT2, KLF2; (b) M2: MEIS1, NKPD1, ONECUT2, OSR1; (c) M3: MEIS1, NKPD1, KLF2, SOX1; (d) M4: ONECUT2, OSR1, SOX1, EOMES; (e) M5: MEIS1, NKPD1, ONECUT2, OSR1, TMEM106A; EPHX3; (f) M6: MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, IRX5; (g) M7: MEIS1, NKPD1, ONECUT2, KLF2, SOX1, TMEM106A; (h) M8: ONECUT2, OSR1, SOX1, EOMES, NID2, VIM; and (i) M9: MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, ITPKB.
 29. The method of any one of claim 18-24 or 26-27, wherein the set of genes consists of one of the following gene sets: (a) M1: MEIS1, NKPD1, ONECUT2, KLF2; (b) M2: MEIS1, NKPD1, ONECUT2, OSR1; (c) M3: MEIS1, NKPD1, KLF2, SOX1; (d) M4: ONECUT2, OSR1, SOX1, EOMES; (e) M5: MEIS1, NKPD1, ONECUT2, OSR1, TMEM106A; EPHX3; (f) M6: MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, IRX5; (g) M7: MEIS1, NKPD1, ONECUT2, KLF2, SOX1, TMEM106A; (h) M8: ONECUT2, OSR1, SOX1, EOMES, NID2, VIM; and (i) M9: MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, ITPKB; and at least one reference gene.
 30. The method of any one of claims 18-29, wherein the method further comprises analyzing the bisulfate converted DNA for mutations in one or both of FGFR3 and TERT.
 31. The method of claim 30, wherein the FGFR3 mutation is FGFR3 is a C to G substitution at chr4:1803568 and wherein the TERT mutation is a G to A substitution at chr5:1295228.
 32. The method of claim 30 or 31, wherein the method further comprises comparing the presence or absence of mutations in FGFR3 and/or TERT to that of the reference set of bladder cancer patients.
 33. A system for quantifying DNA methylation in a urine sediment sample from a bladder cancer patient, comprising (a) at least one cartridge with at least one well, the at least one well comprising primers for amplification of bisulfite modified DNA of at least four genes selected from MEIS1, NKPD1, ONECUT2, KLF2, OSR1, SOX1, EOMES, DDX25, and TMEM106A, and at least one reference gene, wherein the cartridge is configured for performance of methylation-specific quantitative PCR on the genes, and wherein the primers are optionally attached to the at least one well; (b) a detection device for detecting amplified DNA from each gene; and (c) computer software for quantifying the amount and/or concentration of methylated DNA from the genes, wherein the software optionally compares the amount and/or concentration of methylation for the patient to the amount and/or concentration of methylation for a reference set of bladder cancer patients.
 34. The system of claim 33, wherein the cartridge is configured for performance of methylation-specific quantitative PCR on a set of genes comprising MEIS1, NKPD1, ONECUT2, and KLF2, and at least one reference gene.
 35. The system of claim 34, wherein the cartridge is configured for performance of methylation-specific quantitative PCR on a set of genes comprising MEIS1, NKPD1, ONECUT2, and KLF2, and one or more of OSR1, SOX1, EOMES, DDX25, and TMEM106A, and at least one reference gene.
 36. The system of claim 33, 34, or 35, wherein the cartridge is configured for performance of methylation-specific quantitative PCR on a set of genes comprising MEIS1, NKPD1, ONECUT2, and KLF2, and one or more of EPHX3, IRX5, NID2, VIM, and ITPKB, and at least one reference gene.
 37. The system of claim 33, wherein the cartridge is configured for performance of methylation-specific quantitative PCR on a set of genes comprising one of the following gene sets: (a) M1: MEIS1, NKPD1, ONECUT2, KLF2, and at least one reference gene; (b) M2: MEIS1, NKPD1, ONECUT2, OSR1, and at least one reference gene; (c) M3: MEIS1, NKPD1, KLF2, SOX1, and at least one reference gene; (d) M4: ONECUT2, OSR1, SOX1, EOMES, and at least one reference gene; (e) M5: MEIS1, NKPD1, ONECUT2, OSR1, TMEM106A; EPHX3, and at least one reference gene; (f) M6: MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, IRX5, and at least one reference gene; (g) M7: MEIS1, NKPD1, ONECUT2, KLF2, SOX1, TMEM106A, and at least one reference gene; (h) M8: ONECUT2, OSR1, SOX1, EOMES, NID2, VIM, and at least one reference gene; and (i) M9: MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, ITPKB, and at least one reference gene.
 38. The system of claim 33, wherein the cartridge is configured for performance of methylation-specific quantitative PCR on a set of genes consisting of one of the following gene sets: (a) M1: MEIS1, NKPD1, ONECUT2, KLF2, and at least one reference gene; (b) M2: MEIS1, NKPD1, ONECUT2, OSR1, and at least one reference gene; (c) M3: MEIS1, NKPD1, KLF2, SOX1, and at least one reference gene; (d) M4: ONECUT2, OSR1, SOX1, EOMES, and at least one reference gene; (e) M5: MEIS1, NKPD1, ONECUT2, OSR1, TMEM106A; EPHX3, and at least one reference gene; (f) M6: MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, IRX5, and at least one reference gene; (g) M7: MEIS1, NKPD1, ONECUT2, KLF2, SOX1, TMEM106A, and at least one reference gene; (h) M8: ONECUT2, OSR1, SOX1, EOMES, NID2, VIM, and at least one reference gene; and (i) M9: MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, ITPKB, and at least one reference gene.
 39. The system of any one of claims 33-38, wherein the at least one reference gene comprises one or more of CTNS, TOP3A, COL2A and SLC24A3.
 40. The system of any one of claims 33-39, wherein the primers are attached to the at least one well.
 41. The system of any one of claim 33-40, wherein the cartridge further comprises reagents for detecting mutations in one or both of TERT and FGFR3.
 42. The system of claim 41, wherein the FGFR3 mutation is FGFR3 is a C to G substitution at chr4:1803568 and wherein the TERT mutation is a G to A substitution at chr5:1295228.
 43. The system of any one of claims 33-42, wherein the system is capable of performing the method of any one of claims 1-17.
 44. A cartridge comprising at least one well comprising primers for amplification of bisulfite modified DNA of at least four genes selected from MEIS1, NKPD1, ONECUT2, KLF2, OSR1, SOX1, EOMES, DDX25, and TMEM106A, and at least one reference gene, wherein the cartridge is configured for performance of methylation-specific quantitative PCR on the genes, and wherein the primers are optionally attached to the at least one well.
 45. The cartridge of claim 44, wherein the cartridge is configured for performance of methylation-specific quantitative PCR on a set of genes comprising MEIS1, NKPD1, ONECUT2, and KLF2, and at least one reference gene.
 46. The cartridge of claim 44, wherein the cartridge is configured for performance of methylation-specific quantitative PCR on a set of genes comprising MEIS1, NKPD1, ONECUT2, and KLF2, and one or more of OSR1, SOX1, EOMES, DDX25, and TMEM106A, and at least one reference gene.
 47. The cartridge of claim 44, 45, or 46, wherein the cartridge is configured for performance of methylation-specific quantitative PCR on a set of genes comprising MEIS1, NKPD1, ONECUT2, and KLF2, and one or more of EPHX3, IRX5, NID2, VIM, and ITPKB, and at least one reference gene.
 48. The cartridge of claim 44, wherein the cartridge is configured for performance of methylation-specific quantitative PCR on a set of genes comprising one of the following gene sets: (a) M1: MEIS1, NKPD1, ONECUT2, KLF2, and at least one reference gene; (b) M2: MEIS1, NKPD1, ONECUT2, OSR1, and at least one reference gene; (c) M3: MEIS1, NKPD1, KLF2, SOX1, and at least one reference gene; (d) M4: ONECUT2, OSR1, SOX1, EOMES, and at least one reference gene; (e) M5: MEIS1, NKPD1, ONECUT2, OSR1, TMEM106A; EPHX3, and at least one reference gene; (f) M6: MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, IRX5, and at least one reference gene; (g) M7: MEIS1, NKPD1, ONECUT2, KLF2, SOX1, TMEM106A, and at least one reference gene; (h) M8: ONECUT2, OSR1, SOX1, EOMES, NID2, VIM, and at least one reference gene; and (i) M9: MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, ITPKB, and at least one reference gene.
 49. The cartridge of claim 44, wherein the cartridge is configured for performance of methylation-specific quantitative PCR on a set of genes consisting of one of the following gene sets: (a) M1: MEIS1, NKPD1, ONECUT2, KLF2, and at least one reference gene; (b) M2: MEIS1, NKPD1, ONECUT2, OSR1, and at least one reference gene; (c) M3: MEIS1, NKPD1, KLF2, SOX1, and at least one reference gene; (d) M4: ONECUT2, OSR1, SOX1, EOMES, and at least one reference gene; (e) M5: MEIS1, NKPD1, ONECUT2, OSR1, TMEM106A; EPHX3, and at least one reference gene; (f) M6: MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, IRX5, and at least one reference gene; (g) M7: MEIS1, NKPD1, ONECUT2, KLF2, SOX1, TMEM106A, and at least one reference gene; (h) M8: ONECUT2, OSR1, SOX1, EOMES, NID2, VIM, and at least one reference gene; and (i) M9: MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, ITPKB, and at least one reference gene.
 50. The cartridge of any one of claims 44-49, wherein the at least one reference gene comprises one or more of CTNS, TOP3A, COL2A and SLC24A3.
 51. The cartridge of any one of claims 44-50, wherein the primers are attached to the at least one well.
 52. The cartridge of any one of claim 44-51, wherein the cartridge further comprises reagents for determining mutations in one or both of TERT and FGFR3.
 53. The cartridge of claim 52, wherein the FGFR3 mutation is FGFR3 is a C to G substitution at chr4:1803568 and wherein the TERT mutation is a G to A substitution at chr5:1295228.
 54. A kit comprising the cartridge of any one of claims 44-53, and further comprising at least one of the following: (a) deoxyribonucleotide triphosphates dTTP, dATP, dCTP and dGTP; (b) at least one DNA polymerase enzyme; and (c) at least one reaction and/or wash buffer.
 55. The kit of claim 54, wherein the kit comprises each of (a) deoxyribonucleotide triphosphates dTTP, dATP, dCTP and dGTP; (b) at least one DNA polymerase enzyme; and (c) at least one reaction and/or wash buffer.
 56. The kit of claim 54 or 55, wherein the kit further comprises at least one detection reagent.
 57. The kit of any one of claims 54-56, wherein the kit further comprises at least one reagent for bisulfite conversion of DNA.
 58. The kit of any one of claims 54-57, wherein the kit further comprises reagents for reagents for detecting mutations in one or both of TERT and FGFR3.
 59. The kit of claim 58, wherein the FGFR3 mutation is FGFR3 is a C to G substitution at chr4:1803568 and wherein the TERT mutation is a G to A substitution at chr5:
 1295228. 60. The kit of any one of claims 54-59, wherein the kit further comprises instructions for use.
 61. A composition comprising a set of primers for PCR amplification of bisulfite modified DNA of at least four genes selected from MEIS1, NKPD1, ONECUT2, KLF2, OSR1, SOX1, EOMES, DDX25, and TMEM106A, and at least one reference gene.
 62. The composition of claim 61, wherein the genes comprise MEIS1, NKPD1, ONECUT2, and KLF2, and at least one reference gene.
 63. The composition of claim 61, wherein the genes comprise MEIS1, NKPD1, ONECUT2, and KLF2, and one or more of OSR1, SOX1, EOMES, DDX25, and TMEM106A, and at least one reference gene.
 64. The composition of any one of claims 61-63, wherein the genes comprise MEIS1, NKPD1, ONECUT2, and KLF2, and one or more of EPHX3, IRX5, NID2, VIM, and ITPKB, and at least one reference gene.
 65. The composition of claim 61, comprising a set of primers for methylation-specific quantitative PCR of a set of genes comprising: (a) MEIS1, NKPD1, ONECUT2, KLF2, and at least one reference gene; (b) MEIS1, NKPD1, ONECUT2, OSR1, and at least one reference gene; (c) MEIS1, NKPD1, KLF2, SOX1, and at least one reference gene; (d) ONECUT2, OSR1, SOX1, EOMES, and at least one reference gene; (e) MEIS1, NKPD1, ONECUT2, OSR1, TMEM106A; EPHX3, and at least one reference gene; (f) MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, IRX5, and at least one reference gene; (g) MEIS1, NKPD1, ONECUT2, KLF2, SOX1, TMEM106A, and at least one reference gene; (h) ONECUT2, OSR1, SOX1, EOMES, NID2, VIM, and at least one reference gene; or (i) MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, ITPKB, and at least one reference gene.
 66. The composition of claim 61, comprising a set of primers for methylation-specific quantitative PCR of a set of genes consisting of: (a) MEIS1, NKPD1, ONECUT2, KLF2, and at least one reference gene; (b) MEIS1, NKPD1, ONECUT2, OSR1, and at least one reference gene; (c) MEIS1, NKPD1, KLF2, SOX1, and at least one reference gene; (d) ONECUT2, OSR1, SOX1, EOMES, and at least one reference gene; (e) MEIS1, NKPD1, ONECUT2, OSR1, TMEM106A; EPHX3, and at least one reference gene; (f) MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, IRX5, and at least one reference gene; (g) MEIS1, NKPD1, ONECUT2, KLF2, SOX1, TMEM106A, and at least one reference gene; (h) ONECUT2, OSR1, SOX1, EOMES, NID2, VIM, and at least one reference gene; or (i) MEIS1, NKPD1, ONECUT2, KLF2, TMEM106A, ITPKB, and at least one reference gene.
 67. The composition of any one of claims 61-66, wherein the at least one reference gene comprises one or more of CTNS, TOP3A, COL2A and SLC24A3.
 68. The composition of any one of claims 61-67, further comprising reagents for detecting mutations in one or both of TERT and FGFR3.
 69. The composition of claim 68, wherein the FGFR3 mutation is FGFR3 is a C to G substitution at chr4:1803568 and wherein the TERT mutation is a G to A substitution at chr5:1295228.
 70. The composition of any one of claims 61-69, further comprising at least one of the following: (a) deoxyribonucleotide triphosphates dTTP, dATP, dCTP and dGTP; (b) at least one DNA polymerase enzyme; (c) at least one reaction and/or wash buffer.
 71. The composition of claim 70, wherein the composition comprises each of (a) deoxyribonucleotide triphosphates dTTP, dATP, dCTP and dGTP; (b) at least one DNA polymerase enzyme; and (c) at least one reaction and/or wash buffer.
 72. The composition of claim 70 or 71, wherein the composition further comprises at least one detection reagent.
 73. The composition of any one of claims 70-72, wherein the composition further comprises at least one reagent for bisulfate conversion of DNA. 